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1 | first author alphabetical | authors | title | publication year | link | type | journal name | topic | outcome | descriptive study | empirical study | literature review | methodology | results | data | public data | blogs | news outlets | other media | study cases | survey | youtube | targeted country | attacking country | doi | ||||||||
2 | Abdelali | Firoj Alam, Fahim Dalvi, Shaden Shaar, Nadir Durrani, Hamdy Mubarak, Alex Nikolov, Giovanni Da San Martino, Ahmed Abdelali, Hassan Sajjad, Kareem Darwish, and Preslav Nakov | Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms | 2021 | https://arxiv.org/pdf/2007.07996.pdf | Working report | Arxiv | Identify disinformation | Influence political discourse | 0 | 1 | 0 | Here we define a comprehensive annotation schema that goes beyond factuality and potential to do harm, extending to information that could be potentially useful, e.g., for government entities to notice or for social media to promote. Information about a possible cure for COVID-19 should get the attention of a fact-checker, and if proven false. We designed the annotation instructions after careful analysis and discussion, followed by iterative refinement based on observations from the pilot annotation. | we issue a call to arms to the research community and beyond to join the fight by supporting our crowd-sourcing annotation efforts. We plan to support the annotation platforms with fresh tweets. We further plan to release annotation platforms for other languages. Finally, we plan regular releases of the data obtained thanks to the crowdsourcing efforts. | We collected frequent tweets (i.e., such with at least 500 retweets) about COVID-19 in March 2020, in both English and Arabic. We used twarc for crawling. To collect the tweets, we used the following keywords and hashtags for English. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | USA | ||||
3 | Abilov | Anton Abilov, Yiqing Hua, Hana Matatov, Ofra Amir, and Mor Naaman | VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twitter | 2021 | https://arxiv.org/pdf/2101.08210.pdf | Working report | Arxiv | Media consumption | Electoral campaigns | 1 | 0 | 0 | The wide spread of unfounded election fraud claims surrounding the U.S. 2020 election had resulted in undermining of trust in the election, culminating in violence inside the U.S. capitol. Under these circumstances, it is critical to understand the discussions surrounding these claims on Twitter, a major platform where the claims were disseminated. To this end, we collected and released the VoterFraud2020 dataset, a multi-modal dataset with 7.6M tweets and 25.6M retweets from 2.6M users related to voter fraud claims. To make this data immediately useful for a diverse set of research projects, we further enhance the data with cluster labels computed from the retweet graph, each user’s suspension status, and the perceptual hashes of tweeted images. The dataset also includes aggregate data for all external links and YouTube videos that appear in the tweets. | Preliminary analyses of the data show that Twitter’s user suspension actions mostly affected a specific community of voter fraud claim promoters, and exposes the most common URLs, images and YouTube videos shared in the data. | Our data collection process involved streaming Twitter data using a data-driven manually curated set of keywords and hashtags. We report on the span and volume of the collected data, as well as on analyses estimating its coverage. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | USA | USA | ||||
4 | Aceves | William Aceves | Virtual Hatred: How Russia Tried to Start a Race War in the United States | 2019 | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3304223 | Academic Journal | Michigan Journal of Race & Law | Characterize propaganda | Claissify Russian propaganda | 0 | 0 | 1 | This Article examines Russia’s actions and considers whether they violate the international prohibitions against racial discrimination and hate speech. | These were coordinated propaganda efforts. Some Facebook and Twitter posts denounced the Black Lives Matter group; other posts condemned the white nationalist movement. And some called for violence. To be clear, these were posts by fake personas created by Russian operatives. But their effects were real. The purpose of this strategy was to manipulate public opinion on racial issues and disrupt the political process. This Article examines Russia’s actions and considers whether they violate the international prohibitions against racial discrimination and hate speech. | Study cases | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | USA | RUS | ||||
5 | Acker | Amelia Acker | Data Craft: The Manipulation of Social Media Metadata | 2018 | https://datasociety.net/wp-content/uploads/2018/11/DS_Data_Craft_Manipulation_of_Social_Media_Metadata.pdf | Report | Data&Society | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 0 | 1 | This report describes how manipulators use data craft to create disinformation with falsified metadata, specifically platform activity signals. These data about engagement activities can be read by machine-learning algorithms, by platforms, and by humans. Manipulators are getting craftier at evading moderation efforts built upon these metadata categories by using platform features in unexpected ways. This report argues that social media metadata can be read as contextual evidence of manipulation in platforms. Reading metadata as a method to validate or dispute social media data can help us understand the craftiness of media manipulators. | This report argues that reading metadata can help us more fully understand the craft of data work and the many roles of metadata in platforms. It provides avenues for identifying vulnerabilities and for pressuring platforms to do better. It points to some open questions for the future of what web archives of social media data can teach us and what their status will be in the future of disinformation studies. | This report includes three case studies of social media metadata manipulation: politicians’ accounts on Instagram, official U.S. government Twitter accounts, and the Facebook ads purchased by the Russian-based Internet Research Agency | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | RUS | ||||
6 | Adalı | Benjamin D. Horne and Sibel Adalı | This Just In Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News | 2017 | https://arxiv.org/pdf/1703.09398.pdf | Working Paper | International AAAI Conference on Web andSocial Media | Amplify and create disinformation | Fake news | 0 | 1 | 0 | Fake news in most cases is more similar to satire than to real news, leading us to conclude that persuasion in fake news is achieved through heuristics rather than the strength of arguments. | We show overall title structure and the use of proper nouns in titles are very significant in differentiating fake from real. This leads us to conclude that fake news is targeted for audiences who are not likely to read beyond titles and is aimed at creating mental associations between entities and claims. | First, we collected the news stories found in Buzzfeed’s 2016 article on fake election news on Facebook (Silverman 2016). Second, our own political news data set to strengthen our analysis and control for the limitations of the first data set. Our data set contains 75 stories from each of the three defined categories of news: real, fake, and satire. We collected this data by first gathering known real, fake, and satire new sources. Third, Burfoot and Baldwin data set Finally, we use a data set from (Burfoot and Baldwin 2009), which consists of 233 satire news stories and 4000 real news stories used in a classification task between these two types of news stories using lexical and semantic features. | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | NA | NA | ||||
7 | Adamic | Justin Cheng, Lada A. Adamic, P. Alex Dow, Jon Kleinberg, Jure Leskovec | Can Cascades be Predicted? | 2014 | https://dl.acm.org/doi/pdf/10.1145/2566486.2567997 | Academic Journal | Proceedings of the 23rd international conference on World wide web | Identify disinformation | Influence political discourse | 0 | 1 | 0 | We develop a framework for addressing cascade prediction problems. On a large sample of photo reshare cascades on Facebook, we find strong performance in predicting whether a cascade will continue to grow in the future | We find that the relative growth of a cascade becomes more predictable as we observe more of its reshares, that temporal and structural features are key predictors of cascade size, and that initially, breadth, rather than depth in a cascade is a better indicator of larger cascades. This prediction performance is robust in the sense that multiple distinct classes of features all achieve similar performance. We also discover that temporal features are predictive of a cascade’s eventual shape. Observing independent cascades of the same content, we find that while these cascades differ greatly in size, we are still able to predict which ends up the largest. | We sampled our anonymized dataset from photos uploaded to Facebook in June 2013 and observed any re- shares occurring within 28 days of initial upload. | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | UNK | UNK | ||||
8 | Adamic | Eytan Bakshy andSolomon Messing and Lada A. Adamic | Exposure to ideologically diverse news and opinion on Facebook | 2015 | http://science.sciencemag.org/content/348/6239/1130 | Academic Journal | Science | Media consumption | ideological homophily | 0 | 1 | 0 | Using deidentified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks and examined the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook’s algorithmically ranked News Feed and further studied users’ choices to click through to ideologically discordant content. | Compared with algorithmic ranking, individuals’ choices played a stronger role in limiting exposure to cross-cutting content. | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | USA | NA | |||||
9 | Adamic | Adrien Friggeri and Lada A Adamic and Dean Eckles and Justin Cheng. | Rumor cascades | 2014 | https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/viewFile/8122/8110 | Working Paper | International AAAI Conference on Web andSocial Media | Amplify and create disinformation | Fake news | 0 | 1 | 0 | From this sample we infer the rates at which rumors from different categories and of varying truth value are uploaded and reshared | We find that rumor cascades run deeper in the social network than reshare cascades in general. We then examine the effect of individual reshares receiving a comment containing a link to a Snopes article on the evolution of the cascade. We find that receiving such a comment increases the likelihood that a reshare of a rumor will be deleted. Furthermore, large cascades are able to accumulate hundreds of Snopes comments while continuing to propagate. Finally, using a dataset of rumors copied and pasted from one status update to another, we show that rumors change over time and that different variants tend to dominate different bursts in popularity | By referencing known rumors from Snopes.com, a popular website documenting memes and urban legends, we track the propagation of thousands of rumors appearing on Facebook. | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | ||||
10 | Adams | Deen Freelon, Michael Bossetta, Chris Wells, Josephine Lukito, Yiping Xia, and Kirsten Adams | Black Trolls Matter: Racial and Ideological Asymmetries in Social Media Disinformation | 2020 | https://journals.sagepub.com/doi/pdf/10.1177/0894439320914853 | Academic Journal | Social Science Computer Review | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | preliminary evidence has suggested that race may also play a substantial role in determining the targeting and consumption of disinformation content. Such racial asymmetries may exist alongside, or even instead of, ideological ones. Our computational analysis of 5.2 million tweets by the Russian government-funded “troll farm” known as the Internet Research Agency sheds light on these possibilities. | We find stark differences in the numbers of unique accounts and tweets originating from ostensibly liberal, conservative, and Black left-leaning individuals. But diverging from prior empirical accounts, we find racial presentation—specifically, presenting as a Black activist—to be the most effective predictor of disinformation engagement by far. Importantly, these results could only be detected once we disaggregated Black-presenting accounts from non-Black liberal accounts. In addition to its contributions to the study of ideological asymmetry in disinformation content and reception, this study also underscores the general relevance of race to disinformation studies. | On October 17, 2018, Twitter announced two data sets containing tweets posted by suspected IRA accounts. It posted one data set publicly but redacted the screen names, display names, and user IDs of all accounts that had accrued fewer than 5,000 followers at the time of account suspension. Of the 3,667 unique screen names present in this data set, only 167 (4.6%) were left unredacted. This data set was not suitable for the current study because we needed to know each author’s screen name and display name to categorize each tweet according to its author’s sockpuppet identity. Fortunately, Twitter also made an unredacted version of the data set available to researchers who fill out an application detailing how they plan on using it. We submitted this application and were granted access to Twitter’s official, unredacted IRA data set on March 29, 2019. | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | USA | RUS | ||||
11 | Adler-Nissen | Yevgeniy Golovchenko and Mareike Hartmann and Rebecca Adler-Nissen | State, Media and Civil Society in the Information Warfare Over Ukraine: Citizen Curators of Digital Disinformation | 2018 | https://doi.org/10.1093/ia/iiy148 | Academic Journal | International Affairs 94 | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | This article explores the dynamics of digital (dis)information in the conflict between Russia and Ukraine. International Relations scholars have presented the online debate in terms of ‘information warfare’—that is, a number of strategic campaigns to win over local and global public opinion, largely orchestrated by the Kremlin and pro-western authorities. However, this way of describing the online debate reduces civil society to a mere target for manipulation. This article presents a different understanding of the debate. By examining the social media engagement generated by one of the conflict's most important events—the downing of the Malaysian Airlines Flight 17 (MH17) over Ukraine—we explore how competing claims about the cause of the plane crash are disseminated by the state, media and civil society. | By analysing approximately 950,000 tweets, the article demonstrates how individual citizens are more than purveyors of government messages; they are the most active drivers of both disinformation and attempts to counter such information. These citizen curators actively shape competing narratives about why MH17 crashed and citizens, as a group, are four times more likely to be retweeted than any other type of user. Our findings challenge conceptualizations of a state-orchestrated information war over Ukraine, and point to the importance of citizen activity in the struggle over truths during international conflicts. | 950,000 tweets | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | ||||
12 | Agardh-Twetman | James Pamment and Howard Nothhaft and Henrik Agardh-Twetman and Alicia Fjällhed | Countering Information Influence Activities The State of the Art | 2018 | https://www.msb.se/RibData/Filer/pdf/28697.pdf | Report | Lund University | Amplify and create disinformation | Fake news | 0 | 0 | 1 | The report is intended to offer (1) a scientific overview to support the development of the MSB handbook Counter Influence Strategies for Communicators, (2) a guide and framework that can support the development of training and education on counter influence, and (3) a Swedish perspective on the knowledge currently available on information influence activities. | The most important recommendation in this report is therefore to study information operations first, and to act cautiously in trying to mitigate or counter their effects. Crying “wolf!” (or “Fake News!”) at every news item we dislike is a sure way to erode credibility. Our adversaries’ biggest and most effective victories come when we do their work for them. | Fake news | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | ||||
13 | Aidt | Toke S Aidt, Facundo Albornoz and Esther Hauk | Foreign influence and domestic policy | 2021 | https://www.nottingham.ac.uk/research/groups/nicep/documents/working-papers/2020/nicep-2020-01.pdf | Academic Journal | Journal of Economic Literature | Amplify and create disinformation | Literature Review | 0 | 0 | 1 | Foreign influence is a strategic choice aimed at internalizing these externalities and takes three principal forms: (i) voluntary agreements, (ii) policy interventions based on rewarding or sanctioning the target country to obtain a specific change in policy and (iii) institution interventions aimed at influencing the political institutions in the target country. | We propose a unifying theoretical framework to study when foreign influence is chosen and in which form, and use it to organize and evaluate the new political economics literature on foreign influence along with work in cognate disciplines | Literature Review | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | NA | NA | ||||
14 | Akers | John Akers and Gagan Bansal and Gabriel Cadamuro and Christine Chen and Quanze Chen and Lucy Lin and Phoebe Mulcaire and Rajalakshmi Nandakumar and Matthew Rockett and Lucy Simko and John Toman and Tongshuang Wu and Eric Zeng and Bill Zorn and Franziska Roesner | Technology-Enabled Disinformation: Summary, Lessons, and Recommendations | 2018 | https://arxiv.org/abs/1812.09383 | Working Paper | Cornell University | Amplify and create disinformation | Fake news | 0 | 1 | 1 | (1) How technologies and today's technical platforms enable and support the creation and spread of such mis- and disinformation, as well as (2) how technical approaches could be used to mitigate these issues | We summarize the space of technology-enabled mis- and disinformation based on our investigations, and then surface our lessons and recommendations for technologists, researchers, platform designers, policymakers, and use | Study cases | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | NA | NA | ||||
15 | Akoglu | Shebuti Rayana and Leman Akoglu | Collective Opinion Spam Detection: Bridging Review Networks and Metadata | 2015 | http://www.andrew.cmu.edu/user/lakoglu/pubs/15-kdd-collectiveopinionspam.pdf | Working Paper | ACM KDD | Identify disinformation | Fake news | 0 | 0 | 0 | We propose a new holistic approach called SpEagle that utilizes clues from all metadata (text, timestamp, rating) as well as relational data (network), and harness them collectively under a unified framework to spot suspicious users and reviews, as well as products targeted by spam. Moreover, our method can evidently and seamlessly integrate semi-supervision, i.e., a (small) set of labels if available, without requiring any training or changes in its underlying algorithm. | We demonstrate the electiveness and scalability of SpEagle on three real-world review datasets from Yelp.com with filtered (spam) and recommended (no spam) reviews, where it significantly outperforms several baselines and state-of-the-art methods. | Reviews in Yelp, web application | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | ||||
16 | Akoglu | Leman Akoglu and Rishi Chandy and Christos Faloutsos. | Opinion fraud detection in online reviews by network effects | 2013 | https://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/viewFile/5981/6338 | Working Paper | International AAAI Conference on Web andSocial Media | Identify disinformation | New method to check if news are fake | 0 | 1 | 0 | We propose a fast and effective framework, FRAUDEAGLE, for spotting fraudsters and fake reviews in online review datasets | Our method has several advantages: (1) it exploits the network effect among reviewers and products, unlike the vast majority of existing methods that focus on review text or behavioral analysis, (2) it consists of two complementary steps; scoring users and reviews for fraud detection, and grouping for visualization and sensemaking, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available, and (4) it is scalable to large datasets as its run time grows linearly with network size | Literature review | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | NA | NA | ||||
17 | Al-Hassani | Jim Maddock and Kate Starbird and Haneen Al-Hassani and Daniel E. Sandoval and Mania Orand and Robert M. Mason | Characterizing Online Rumoring Behavior Using Multi-Dimensional Signatures | 2015 | https://doi.org/10.1145/2675133.2675280 | Report | Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing | Identify disinformation | Fake news | 0 | 1 | 0 | In this study, the authors examine four rumours that spread via Twitter after the 2013 Boston Marathon Bombings. They establish connections between quantitative measures and the qualitative “story” of rumours, revealing differences among rumour types. In doing so, they construct multi-dimensional signatures (patterns of information flow over time and across other features) that describe a rumour’s temporal progression of rumour spreading behavior (within individual tweets), URL domain diversity, domains over time, lexical diversity, and geolocation information. Each tweet within each rumour subset was coded into one of seven distinct categories related to the rumour behavior type: Misinformation (relaying rumour as fact). Speculation (develop or support growing rumour). Correction (clearly engate rumour). Question (actively challenges rumour). Hedge (passes along rumour but with doubt). Unrelated, or neutral/other (position unclear or neutral to researcher) | From their study, lexical diversity appears to correlate with different kinds of rumour spreading behavior. Speculation, for example, has higher lexical diversity than misinformation. They find that rumours with low lexical diversity spread without much substantial content variation, while rumours with high lexical diversity tend to be more “conversational” and appear to engage users in “collaborative sensemaking.” | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | |||||
18 | Al-Rawi | Ahmed Al-Rawi | Gatekeeping Fake News Discourses on Mainstream Media Versus Social Media | 2019 | https://journals.sagepub.com/doi/pdf/10.1177/0894439318795849?casa_token=ImSTpmmFjTQAAAAA:qll4EpUa6GNEJi9Rpg8iAA7t6rLFWgZgrzybS8i5PQFlw49DpjZRkENW-SCeCFxzX90vYUGKMewW3w | Academic Journal | Social Science Computer Review | Amplify and create disinformation | Influence political discourse | 0 | 1 | 0 | This study analyzes mainstream media (MSM) coverage of fake news discourse and compares it with social networking sites (SNS) users who reference the term “fakenews” in their tweets. The study employs computational methods by analyzing over 8 million tweets and 1,350 news stories using topic modeling | Building on the theory of (networked) gatekeeping and Herman and Chomsky’s propaganda model, the results show that SNS users follow networked gatekeeping practices by mostly associating fake news references to the alleged bias of MSM. On the other hand, MSM coverage tends to link fake news to SNS’s negative role in spreading misinformation. I argue here that there is a networked flak activity on Twitter which is defined as a collective negative response to MSM in order to discipline it, change its tone and editorial stance, or undermine the public’s trust in it. | Two sets of large data were collected. The first set consisted of 8,116,792 tweets which referenced “#fakenews” was retrieved from Twitter for a period of over 7 months (from January to August 2017) using Boston University Twitter platform called Boston University-Twitter Collection and Analysis Toolkit | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | USA | ||||
19 | Alatawi | Kai Shu, Amrita Bhattacharjee, Faisal Alatawi, Tahora H. Nazer, Kaize Ding, Mansooreh Karami, and Huan Liu | Combating disinformation in a social media age | 2020 | https://wires.onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1385?casa_token=GlPDnNW41AUAAAAA%3ArJckXMpVAAsTdd09TKrXAlhZcDWgFoaIfIC_QNVJV66vUPV-0-tsCAw5gNXavfo728q03_AB-iFRxqZo | Academic Journal | Data Mining and Knowledge Discovery | Identify disinformation | Literature Review | 1 | 0 | 0 | We introduce different forms of disinformation, discuss factors related to the spread of disinformation, elaborate on the inherent challenges in detecting disinformation. | Show some approaches to mitigating disinformation via education, research, and collaboration. Looking ahead, we present some promising future research directions on disinformation | Literature Review | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | NA | NA | ||||
20 | Albright | Renee DiResta and Kris Shaffer and Becky Ruppel and David Sullivan and Robert Matney and Ryan Fox and Jonathan Albright and Ben Johnson | The Tactics & Tropes of the Internet Research Agency | 2018 | https://disinformationreport.blob.core.windows.net/disinformation-report/NewKnowledge-Disinformation-Report-Whitepaper.pdf | Report | New Knowledge | amplify and create disinformation | Effects of propaganda | 0 | 1 | 0 | They analyze the database and present several summary statistics | This investigation of the Internet Research Agency’s activities and tactics highlights complex technological, social, and cognitive vulnerabilities. Throughout its multi-year effort, the Internet Research Agency exploited divisions in our society by leveraging vulnerabilities in our information ecosystem. They exploited social unrest and human cognitive biases. The divisive propaganda Russia used to influence American thought and steer conversations for over three years wasn’t always objectively false. The content designed to reinforce in-group dynamics would likely have offended outsiders who saw it, but the vast majority wasn’t hate speech. Much of it wasn’t even particularly objectionable. But it was absolutely intended to reinforce tribalism, to polarize and divide, and to normalize points of view strategically advantageous to the Russian government on everything from social issues to political candidates. It was designed to exploit societal fractures, blur the lines between reality and fiction, erode our trust in media entities and the information environment, in government, in each other, and in democracy itself. This campaign pursued all of those objectives with innovative skill, scope, and precision | The comprehensive dataset included: ~10.4 million tweets (of which ~6 million were original) across 3841 twitter accounts ~1100 YouTube videos across 17 account channels ~116,000 Instagram posts across 133 accounts ~61,500 unique Facebook posts across 81 Pages There were ~77 million engagements on Facebook, ~187 million engagements on Instagram, and ~73 million engagements on original content on Twitter. Precise summary statistics are presented later in this report. | 0 | 1 | 1 | 0 | 9 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | USA | RUS | ||||
21 | Alexanyan | Karina Alexanyan and Vladimir Barash and Bruce Etling and Robert Faris and Urs Gasser and John Kelly and John G. Palfrey and Hal Roberts | Exploring Russian Cyberspace: Digitally-Mediated Collective Action and the Networked Public Sphere | 2012 | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2014998 | Working Paper | SSRN | Characterize propaganda | Computational propaganda | 0 | 1 | 0 | This paper summarizes the major findings of a three-year research project to investigate the Internet’s impact on Russian politics, media and society. We employed multiple methods to study online activity: the mapping and study of the structure, communities and content of the blogosphere; an analogous mapping and study of Twitter; content analysis of different media sources using automated and human-based evaluation approaches; and a survey of bloggers; augmented by infrastructure mapping, interviews and background research. | We find the emergence of a vibrant and diverse networked public sphere that constitutes an independent alternative to the more tightly controlled offline media and political space, as well as the growing use of digital platforms in social mobilization and civic action. Despite various indirect efforts to shape cyberspace into an environment that is friendlier towards the government, we find that the Russian Internet remains generally open and free, although the current degree of Internet freedom is in no way a prediction of the future of this contested space | Tweets. Blogs. Surveys. | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | NA | NA | ||||
22 | Alexanyan | John Kelly and Vladimir Barash and Karina Alexanyan and Bruce Etling and Robert Faris and Urs Gasser and John G. Palfrey | Mapping Russian Twitter | 2012 | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2028158 | Working Paper | SSRN | Characterize propaganda | Computational propaganda | 0 | 1 | 0 | Drawing from a corpus of over 50 million Russian-language tweets collected between March 2010 and March 2011, we created a network map of 10,285 users comprising the ‘discussion core,’ and clustered them based on a combination of network features. The resulting segmentation revealed key online constituencies active in Russian Twitter | The major topical groupings in Russian Twitter include: Political, Instrumental, CIS Regional, Technology, and Music. There are also several clusters centered on Russian regions, which is significant given the limited reach of the Internet in the regions outside of Moscow and St. Petersburg. Russian Twitter features a great deal of activity generated by marketing campaigns and search engine optimization (SEO) initiatives, including both automated and coordinated human actors. After our initial mapping resulted in a network dominated by these ‘instrumental’ actors, we constructed a filter to limit their presence in the network and discover relationships among a wider variety of ‘organic’ actors. | Tweets. 50 million Russian-language tweets collected between March 2010 and March 2011 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | ||||
23 | Alexanyan | John Kelly and Vladimir Barash and Karina Alexanyan and Bruce Etling and Robert Faris and Urs Gasser and John Palfrey | Mapping Russian Twitter | 2012 | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2028158 | working paper | The Berkman Center for Internet and Society at Harvard University | amplify and create disinformation | Effects of propaganda | 0 | 1 | 0 | The Twitter data set is based on an index of Russian twitter users listed by the popular Russian language search engine Yandex. From these tweets, we extracracted the complete network of 'mentions' which occur when Twitter user A mentions Twitter user B by including B's Twitter user name preceded by an @ charachter in A's tweet. This allowed us to see the complete network of direct dynamic relationships. | The major topical groupings in Russian Twitter include: Political, Instrumental, CIS Reigonal, Technology, and Music. There are also several clusters centered on Russian regions which is significant. Russian twitetr features a great deal of activity generated by marketing campaigns anf search engines optimizations. Similar to the Russian blogsphere, the twitter network includes a democratic opposition clusters identified in weblog and twitter networks display. While Other clusters within twitter often mirrored those in Weblogs. | 50 million Russian-language tweets | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | ||||
24 | Alexanyan | Bruce Etling and Karina Alexanyan and John Kelly and Robert Faris and John G. Palfrey and Urs Gasser | Public Discourse in the Russian Blogosphere: Mapping RuNet Politics and Mobilization | 2010 | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1698344 | Working Paper | SSRN | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | We analyzed Russian blogs to discover networks of discussion around politics and public affairs. Beginning with an initial set of over five million blogs, we used social network analysis to identify a highly active ‘Discussion Core’ of over 11,000. These were clustered according to long term patterns of citations within posts, and the resulting segmentation characterized through both automated and human content analysis. | ´- Unlike their counterparts in the U.S. and elsewhere, Russian bloggers prefer platforms that combine features typical of blogs with features of social network services (SNSs) like Facebook. Russian blogging is dominated by a handful of these "SNS hybrids." - While the larger Russian blogosphere is highly divided according to platform, there is a central Discussion Core that contains the majority of political and public affairs discourse. This core is comprised mainly, though not exclusively, of blogs on the LiveJournal platform. - The Discussion Core features four major groupings: — Politics and Public Affairs (including news-focused discussion, business and finance, social activists, and political movements) — Culture (including literature, cinema, high culture, and popular culture) — Regional (bloggers in Belarus, Ukraine, Armenia, Israel, etc.) — Instrumental (paid blogging and blogging for external incentives) - Political/public affairs bloggers cover a broad spectrum of attitudes and agendas and include many who discuss politics from an independent standpoint, as well as those affiliated with offline political and social movements, including strong ‘Democratic Opposition’ and 'Nationalist' clusters. - The Russian political blogosphere supports more cross-linking debate than others we have studied (including those of the U.S. and Iran), and appears less subject to the formation of self-referential 'echo chambers.' - Pro-government bloggers are not especially prominent and do not constitute their own cluster, but are mostly located in a part of the network featuring general discussion of Russian public affairs. However, there is a concentration of bloggers affiliated with pro-government youth groups among the Instrumental bloggers. - We find evidence of political and social mobilization, particularly in those clusters affiliated with offline political and social movements. - The online 'news diet' of Russian bloggers is more independent, international, and oppositional than that of Russian Internet users overall, and far more so than that of non-Internet users, who are more reliant upon state-controlled federal TV channels. - Popular political YouTube videos focus on corruption and abuse of power by elites, the government, and the police. | Five millions of blogs | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | ||||
25 | Alfaro | Eugenio Tacchini and Gabriele Ballarin and Marco L Della Vedova and Stefano Moret and Luca de Alfaro | Some like it hoax: Automated fake news detection in social networks | 2017 | https://arxiv.org/pdf/1704.07506.pdf | Working Paper | Cornell University | Identify disinformation | Fake news | 0 | 1 | 0 | As a contribution towards this objective, we show that Facebook posts can be classified with high accuracy as hoaxes or non-hoaxes on the basis of the users who “liked” them. We present two classification techniques, one based on logistic regression, the other on a novel adaptation of Boolean crowdsourcing algorithms. On a dataset consisting of 15,500 Facebook posts and 909,236 users, we obtain classification accuracies exceeding 99% even when the training set contains less than 1% of the posts. | We further show that our techniques are robust: they work even when we restrict our attention to the users who like both hoax and non-hoax posts. These results suggest that mapping the diffusion pattern of information can be a useful component of automatic hoax detection systems. | Facebook posts | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | ||||
26 | Alizadeh | Meysam Alizadeh, Jacob N. Shapiro, Cody Buntain and, Joshua A. Tucker. | Content-based features predict social media influence operations | 2020 | https://advances.sciencemag.org/content/6/30/eabb5824 | Academic Journal | Science | Identify disinformation | Influence political discourse | 0 | 1 | 0 | We study how easy it is to distinguish influence operations (IO) from organic social media activity by assessing the performance of a machine learning approach. Our method uses public activity to detect content that is part of IOs based on features derived solely from content. | To assess how well content-based features distinguish these influence operations from random samples of general and political American users, we train and test classifiers on a monthly basis for each campaign across five prediction tasks. Content-based features perform well across period, country, platform, and prediction task. Industrialized production of influence campaign content leaves a distinctive signal in user-generated content that allows tracking of campaigns from month to month and across different accounts. | We test this method on publicly available Twitter data on Chinese, Russian, and Venezuelan troll activity targeting the United States, as well as the Reddit dataset of Russian influence efforts. | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | USA | CHN, RUS, VEN | ||||
27 | Allcott | Hunt Allcott and Matthew Gentzkow | Social media and fake news in the 2016 election | 2017 | https://www.aeaweb.org/articles?id=10.1257/jep.31.2.211 | Academic Journal | Journal of Economic Perspective | Identify disinformation | Profits of fake news | 0 | 1 | 0 | Our goal in this paper is to offer theoretical and empirical background to frame this debate (Following the 2016 election, a specific concern has been the effect of false stories—“fake news,” as it has been dubbed—circulated on social media). We begin by discussing the economics of fake news. We sketch a model of media markets in which firms gather and sell signals of a true state of the world to consumers who benefit from inferring that state. We conceptualize fake news as distorted signals uncorrelated with the truth. Fake news arises in equilibrium because it is cheaper to provide than precise signals, because consumers cannot costlessly infer accuracy, and because consumers may enjoy partisan news. Fake news may generate utility for some consumers, but it also imposes private and social costs by making it more difficult for consumers to infer the true state of the world—for example, by making it more difficult for voters to infer which electoral candidate they prefer. We then present new data on the consumption of fake news prior to the election. We draw on web browsing data, a new 1,200-person post-election online survey, and a database of 156 election-related news stories that were categorized as false by leading fact-checking websites in the three months before the election. | In the aftermath of the 2016 US presidential election, it was alleged that fake news might have been pivotal in the election of President Trump. We do not provide an assessment of this claim one way or another | Fake news articles | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | ||||
28 | Allcott | Hunt Allcott and Luca Braghieri and Sarah Eichmeyer and and Matthew Gentzkow | The Welfare Effects of Social Media | 2019 | http://web.stanford.edu/~gentzkow/research/facebook.pdf | Working Paper | Stanford University | Media consumption | Facebook activities | 0 | 1 | 0 | We present a randomized evaluation of the welfare effects of Facebook, focusing on US users in the runup to the 2018 midterm election. We measured the willingness-to-accept of 2,844 Facebook users to deactivate their Facebook accounts for four weeks, then randomly assigned a subset to actually do so in a way that we verified | Using a suite of outcomes from both surveys and direct measurement, we show that Facebook deactivation (i) reduced online activity, including other social media, while increasing offline activities such as watching TV alone and socializing with family and friends; (ii) reduced both factual news knowledge and political polarization; (iii) increased subjective well-being; and (iv) caused a large persistent reduction in Facebook use after the experiment. We use participants’ pre-experiment and post-experiment Facebook valuations to quantify the extent to which factors such as projection bias might cause people to overvalue Facebook, finding that the magnitude of any such biases is likely minor relative to the large consumer surplus that Facebook generates. | Facebook. Between September 24th and October 3rd, we recruited participants using Facebook ads. Our ad said, “Participate in an online research study about internet browsing and earn an easy $30 in electronic gift cards. | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | USA | NA | ||||
29 | Allcott | Hunt Allcott and Matthew Gentzknow and Chuan Yu | Trends in the Diffusion of Misinformation on Social Media | 2018 | http://web.stanford.edu/~gentzkow/research/fake-news-trends.pdf | Working Paper | Cornell University | Amplify and create disinformation | Interactions with fake accounts | 0 | 1 | 0 | They measure the trends in diffusion of content from Facebook and Twitter | User interactions with false content rose steadily on both Facebook and Twitter through the end of 2016. Since then, however, interactions with false content have fallen sharply on Facebook while continuing to rise on Twitter, with the ratio of Facebook engagements to Twitter shares decreasing by 60 percent. In comparison, interactions with other news, business, or culture sites have followed similar trends on both platforms. Our results suggest that Facebook’s efforts to limit the diffusion of misinformation after the 2016 election may have had a meaningful impact. | 570 fake news websites and 10,240 fake news stories on Facebook and Twitter | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | ||||
30 | Allcott | Hunt Allcott, Luca Braghieri, Sarah Eichmeyer, and Matthew Gentzkow | The welfare effects of social media | 2020 | https://www.nber.org/system/files/working_papers/w25514/w25514.pdf | Academic Journal | American Economic Review | Media consumption | Well being | 0 | 1 | 0 | Clicking on the ad took the participant to a brief pre-screen survey, which included several background demographic questions and the consent form. 17,335 people passed the pre-screen, by reporting being a US resident born between the years 1900 and 2000 who uses Facebook more than 15 minutes and no more than 600 minutes per day. Of those people, 7,455 consented to participate in the study. After completing the consent form, participants began the baseline survey. The baseline recorded email addresses, additional demographics, and a range of outcome variables. We also asked for each participant’s name, zip code, Twitter handle, and phone number (“in order for us to send you text messages during the study”), as well as the URL of their Facebook profile page (which we would use “solely to observe whether your Facebook account is active”). | we find that deactivating Facebook for the four weeks before the 2018 US midterm election (i) reduced online activity, while increasing offline activities such as watching TV alone and socializing with family and friends; (ii) reduced both factual news knowledge and political polarization; (iii) increased subjective well-being; and (iv) caused a large persistent reduction in post-experiment Facebook use. Deactivation reduced post-experiment valuations of Facebook, suggesting that traditional metrics may overstate consumer surplus. | Between September 24th and October 3rd, we recruited participants using Facebook ads. Our ad said, “Participate in an online research study about internet browsing and earn an easy $30 in electronic gift cards.” Appendix Figure A1 presents the ad. To minimize sample selection bias, the ad did not hint at our research questions or suggest that the study was related to social media or Facebook deactivation. We targeted the ads by demographic cells in an attempt to gather an initial sample that was approximately representative of Facebook users on gender, age, college completion, and political ideology. 1,892,191 unique users were shown the ad, of whom 32,201 clicked on it. | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | USA | USA | ||||
31 | Allen | Jennifer Allen, Antonio Arechar, Gordon Pennycook, David Rand | Scaling Up Fact-Checking Using the Wisdom of Crowds | 2021 | https://psyarxiv.com/9qdza/ | Academic Journal | Science Advances | Media consumption | Combating fake news | 0 | 1 | 0 | "Using a set of 207 news articles flagged for fact-checking by an internal Facebook algorithm, we compare the accuracy ratings given by (i) three professional fact-checkers after researching each article and (ii) 1,128 Americans from Amazon Mechanical Turk after simply reading the headline and lede sentence." | "We find that the average rating of a small politically-balanced crowd of laypeople is as correlated with the average fact-checker rating as the fact-checkers’ ratings are correlated with each other. Furthermore, the layperson ratings can predict whether the majority of fact-checkers rated a headline as “true” with high accuracy, particularly for headlines where all three fact-checkers agree. We also find that layperson cognitive reflection, political knowledge, and Democratic Party preference are positively related to agreement with fact-checker ratings; and that informing laypeople of each headline’s publisher leads to a small increase in agreement with fact-checkers. Our results indicate that crowdsourcing is a promising approach for helping to identify misinformation at scale." | "207 news articles flagged for fact-checking by an internal Facebook algorithm" | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10.31234/osf.io/9qdza | |||||
32 | Almond | Douglas Almond, Xinming Du, and Alana Vogel | Russian Holidays Predict Troll Activity 2015-2017 | 2020 | https://www.nber.org/system/files/working_papers/w28035/w28035.pdf | Working Paper | NBER | Characterize propaganda | Influence of a foreign country in the US | 0 | 1 | 0 | While international election interference is not new, Russia is credited with “industrializing” trolling on English-language social media platforms. In October 2018, Twitter retrospectively identified 2.9 million English-language tweets as covertly written by trolls from Russia's Internet Research Agency. Most active 2015-2017, these Russian trolls generally supported the Trump campaign (Senate Intelligence Committee, 2019) and researchers have traced how this content disseminated across Twitter. Here, we take a different tack and seek exogenous drivers of Russian troll activity | We find that trolling fell 35% on Russian holidays and to a lesser extent, when temperatures were cold in St. Petersburg. More recent trolls released by Twitter do not show any systematic relationship to holidays and temperature, although substantially fewer of these that have been made public to date. Our finding for the pre-2018 interference period may furnish a natural experiment for evaluating the causal effect of Russian trolling on indirectlyaffected outcomes and political behaviors — outcomes that are less traceable to troll content and potentially more important to policymakers than the direct dissemination activities previously studied. As a case in point, we describe suggestive evidence that Russian holidays impacted daily trading prices in 2016 election betting markets. | Twitter dataset | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | RUS | ||||
33 | Alsmadi | Izzat Alsmadi and Michael J.O'Brien | How Many Bots in Russian Troll Tweets? | 2020 | https://www.sciencedirect.com/science/article/pii/S0306457320307986?casa_token=j3LdYXlkhBoAAAAA:UPRnlFn0zZ8xBbERB72gHHBDRsQxa5zWSXd3zxHfBCyvo9R7Uo72sod9xDD-SMFPFfBJuaqYhQ | Academic Journal | Information Processing and Management | Amplify and create disinformation | Bots behavior | 0 | 1 | 0 | Here we use several public Twitter datasets to build a model that can predict whether or not an account is a bot account based on features extracted at the tweet or the account level. We then apply the model to Twitter's Russian Troll Tweets dataset. At the account level, we evaluate features related to how often Twitter accounts are tweeting, as previous research has shown that bots are very active at some account levels and very low at others. | At the tweet level, we noticed that bot accounts tend to sound more formal or structured, whereas real user accounts tend to be more informal in that they contain more slang, slurs, cursing, and the like. We also noted that bots can be created for a range of different goals (e.g., marketing and politics) and that their behaviors vary based on those distinct goals. Ultimately, for high bot-prediction accuracy, models should consider and distinguish among the different goals for which bots are created. | Twitter made the dataset public as part of an investigation of possible influence in the 2016 U.S. presidential election, as trolls attempted to influence a broad range of political debates (Shane & Mazzetti, 2918). The majority of the accounts are affiliated with the Internet Research Agency (IRA), a well-known Russian troll factory (Linvill, Boatwright, Grant & Warren, 2019). The majority of the others are from Iran and Venezuela (Romm, 2019). Only 33% of the English-language tweets contain a hashtag, but during the weeks leading up to the election, the troll dataset witnessed a significant increase in activity, with hundreds of thousands of tweets/retweets per week. | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | MUL | RUS | ||||
34 | Althoff | Galen Weld, Maria Glenski, and Tim Althoff | Political Bias and Factualness in News Sharing across more than 100,000 Online Communities | 2021 | https://ojs.aaai.org/index.php/ICWSM/article/view/18104 | Academic Journal | Proceedings of the International AAAI Conference on Web and Social Media | 0 | 1 | 0 | we conduct the largest study of news sharing on reddit to date, analyzing more than 550 million links spanning 4 years. We use non-partisan news source ratings from Media Bias/Fact Check to annotate links to news sources with their political bias and factualness. | We find that, compared to left-leaning communities, right-leaning communities have 105% more variance in the political bias of their news sources, and more links to relatively-more biased sources, on average. We observe that reddit users’ voting and re-sharing behaviors generally decrease the visibility of extremely biased and low factual content, which receives 20% fewer upvotes and 30% fewer exposures from crossposts than more neutral or more factual content. This suggests that reddit is more resilient to low factual content than Twitter. We show that extremely biased and low factual content is very concentrated, with 99% of such content being shared in only 0.5% of communities, giving credence to the recent strategy of community-wide bans and quarantines. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | |||||||
35 | Amazeen | Michelle A Amazeen | Journalistic interventions: The structural factors affecting the global emergence of fact-checking | 2017 | https://journals.sagepub.com/doi/abs/10.1177/1464884917730217 | Academic Journal | Journalism | Media consumption | Fake news | 0 | 1 | 0 | Since the emergence of FactCheck.org in the United States in 2003, fact-checking interventions have expanded both domestically and globally. The Duke Reporter’s Lab identified nearly 100 active initiatives around the world in 2016. Building off of previous exploratory work by Amazeen, this research utilizes the framework of critical juncture theory to examine why fact-checking interventions are spreading globally at this point in time. Seen as a professional reform movement in the journalistic community, historical research on reform movements suggests several possible factors influencing the emergence of fact-checking such as a decline in journalism, easy access to technology for the masses, and socio-political strife. | This study offers empirical support that fact-checking may be understood as a democracy-building tool that emerges where democratic institutions are perceived to be weak or are under threat and examines similarities between the growth of fact-checking interventions and previous consumer reform movements. As politics increasingly adopts strategies orchestrated by marketing and advertising consultants and agencies – exemplified in the Brexit referendum – political fact-checking may benefit from examining the path of consumer reform movements. For, before fact-checking can be effective at informing individuals, it must first establish itself within a structural environment. | The presence and quantity of fact-checking organizations in countries around the world were based upon the Duke Reporter’s Lab annual census (N=143) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | NA | Na | ||||
36 | Anderson | Simon Hegelich and Michael Rondinelli and Geoffrey T Anderson and Kolja Hegelich and Morteza Shahrezaye and Claudio Santiago Ribeiro and Sybren Daniel Smith and Moisés De La Cruz and John Nicholas Shemelynce and Pratik Desai and Felippe Morais Bicudo | Automated processing of panoramic video content using machine learning techniques | 2017 | https://patentimages.storage.googleapis.com/c3/e4/17/5c839ddad76944/US20170195561A1.pdf | Academic Journal | U.S. Patent Application | Identify disinformation | Processing video | 0 | 1 | 0 | The present disclosure provides techniques for capturing, processing, and displaying panoramic content such as video content and image data with a panoramic camera system. In one embodiment, a method for processing panoramic video content may include communicating captured video content to a virtual sensor of a panoramic camera; applying a machine learning algorithm to the captured video content; identifying content of interest information suitable for use by at least one smart application; and executing a smart application in connection with the identified content of interest information. The machine learning algorithm may include at least one of a pattern recognition algorithm or an object classification algorithm. Examples of smart applications include executing modules for automatically panning movement of the camera field of view, creating video content focused on the content of interest, and warninga user of objects, obstacles, vehicles, or other potential ards in the vicinity of the panoramic camera. | Method to process video | Video Imagines | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | USA | NA | ||||
37 | Andrews | Cynthia Andrews and Elodie Fichet and Yuwei Ding and Emma S. Spiro and Kate Starbird | Keeping Up with the Tweet-dashians: The Impact of ‘Official’ Accounts on Online Rumoring | 2016 | https://doi.org/10.1145/2818048.2819986 | Working Paper | ACM Conference on Computer Supported Cooperative Work and Social Computing companion. ACM | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | This paper examines how “official” accounts participate in the propagation and correction of online rumors in the context of crisis events. Using an emerging method for interpretive analysis of “big” social data, we investigate the spread of online rumors through digital traces—in this case, tweets. | Our study suggests that official accounts can help to slow the spread of a rumor by posting a denial, and— supported by reflections from an organization that recently dealt with a rumor-crisis—offers best practices for organizations around social media strategies and protocols. Based on tweet data and connections to existing literature, we also demonstrate and discuss how mainstream media participate in rumoring, and note the role of a new breed of online media, “breaking news” accounts. This analysis offers a complementary perspective to existing studies that use surveys and interviews to characterize the role official accounts play in online rumoring. | Our analysis examines the spread of rumors on Twitter during crisis events, primarily using a digital record of tweets related to a specific crisis event, collected in real- time using the Twitter Streaming API | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | ||||
38 | Ang | Ullrich K. H. Ecker and Li Chang Ang | Political Attitudes and the Processing of Misinformation Corrections | 2018 | https://onlinelibrary.wiley.com/doi/abs/10.1111/pops.12494 | Academic Journal | Political Psychology | Media consumption | Fake news | 0 | 1 | 0 | The present study used political misinformation—specifically fictional scenarios involving misconduct by politicians from left‐wing and right‐wing parties—and tested participants identifying with those political parties. | Results showed that in this type of scenario, partisan attitudes have an impact on the processing of retractions, in particular (1) if the misinformation relates to a general assertion rather than just a specific singular event and (2) if the misinformation is congruent with a conservative partisanship. | Experiment 1 used (fictional) political scenarios and tested participants scoring on the extreme ends of a bipolar political party-preference scale (the study was conducted in Australia, where the two major political parties are the left-leaning Labor party and the right-leaning Liberal party). | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | USA | NA | ||||
39 | Angelopoulos | Elena Georgiadou, Spyros Angelopoulos, and HelenDrake | Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes | 2020 | https://www.sciencedirect.com/science/article/pii/S0268401219309454 | Academic Journal | International Journal of Information Management | Identify disinformation | Influence political discourse | 0 | 1 | 0 | We use SA, which is one of the most widely applied text mining techniques for SM (Pang & Lee, 2006), and it is a sub-field of Natural Language Processing (NLP). Its aim is to gauge the sentiment, attitude and emotion of a speaker or writer based on the computational treatment of subjectivity in a text (Pang & Lee, 2006; Stieglitz et al., 2014) | We show that SA of tweets has potential as a real-time barometer of public sentiment towards negotiating outcomes to inform government decision-making. Despite the increasing need for information on collective preferences regarding possible negotiating outcomes, negotiators have been slow to capitalise on BDA | Our study is based on two data sources, as follows: i) a timeline of key developments between May 5th and November 7th, 2018, deemed as the most active periods of Brexit negotiations. A select sample of relevant key events is displayed in the annotated graph, while a more complete compilation of the events we explored for the purposes of our study is presented in Appendix A; and ii) a corpus of 13,018,367 tweets published during the same time period, and collected with the use of the Twitter Streaming API to create the dataset of our study | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | GBR | GBR | ||||
40 | Anise | Jordan Wright and Olabode Anise | Don’t at Me: Hunting Twitter Bots at Scale | 2017 | https://www.bellingcat.com/resources/how-tos/2017/06/30/advanced-guide-verifying-video-content/ | Report | Duo Labs | Identify disinformation | Fake news | 0 | 1 | 0 | Using machine learning and publicly available data from Twitter’s API to test a variety of “attributes” to detect bots and botnets, the researchers demonstrate that organized botnets are still active on Twitter and can be discovered. As part of their dataset, they collected 88 million public Twitter accounts, including screen names, tweet counts, followers/following counts, bios, and tweet content. During their analysis they detect a cryptocurrency scam botnet made up of 15,000 bots and identify tactics used by bots to appear credible while avoiding detection. | The authors find that the Random Forest classifier proved to perform the best irrespective of the bot data used for training. The cryptocurrency botnet the researchers discovered could be described as a three-level hierarchy, which “consisted of the scam publishing bots, the hub accounts (if any) the bots were following, and the amplification bots that like each created tweet.” The cryptocurrency spam botnet spoofed legitimate cryptocurrency accounts before transitioning to spoofing celebrity and high-profile accounts. Minor edits were done to profile photos to evade detection. Spoofed cryptocurrency accounts made use of typos of the spoofed account’s name. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | |||||
41 | Applebaum | Chloe Colliver, Peter Pomerantsev, Anne Applebaum, Jonathan Birdwell | Smearing Sweden International Influence Campaigns in the 2018 Swedish Election | 2018 | http://www.lse.ac.uk/iga/assets/documents/arena/2018/Sweden-Report-October-2018.pdf | Report | LSE | Characterize propaganda | Various avenues of influence | 0 | 0 | 0 | Social media listening tools were used to monitor known topics of interest to far-right and pro-Kremlin groups, such as immigration, integration, NATO and ‘Swexit’. When potential opportunities for information campaigns emerged, such as the forest fires or car arson attacks, ISD established monitors to collect relevant information flows to analyse the data for examples of disinformation or coordinated media manipulation. We also tracked election hashtags, political party social media accounts and those of key election candidates to identify any suspicious activity. | Attempts to sway campaign largely from domestic far right, though foreign far right and Russian sources also apparent | Mostly social media tracking and analysis 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | SWE | RUS | ||||
42 | Aral | Sinan Aral and Deb Roy and Soroush Vosougui | The spread of true and false news online | 2018 | http://science.sciencemag.org/content/359/6380/1146 | Academic Journal | Science | Identify disinformation | False news reaches more people than true news | 0 | 1 | 0 | We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications | Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information | The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | ||||
43 | Arayankalam | Jithesh Arayankalam and Satish Krishnan | Relating foreign disinformation through social media, domestic online media fractionalization, government's control over cyberspace, and social media-induced offline violence: Insights from the agenda-building theoretical perspective | 2021 | https://www.sciencedirect.com/science/article/pii/S0040162521000937?casa_token=gR75K1zXq_UAAAAA:vHfED0zZ2Ar3QWnlkJ6kPSwa_NDY25ccFlwsCETJe4bwgXsKc2dJcDyyVJS2b4FHm9Rw8TSoUw | Academic Journal | Social Change | Characterize propaganda | Misinformation | 0 | 1 | 0 | By grounding the discussion on the agenda-building theory, we theorize the relationships among four key variables of interest, namely, (1) foreign disinformation through social media, (2) domestic online media fractionalization, (3) government's control over cyberspace, and (4) social media-induced offline violence in a country. A quantitative analysis based on publicly available archival data offers support for our research model. | Specifically, our findings indicate that foreign disinformation through social media increases social media-induced offline violence in a country by increasing its domestic online media fractionalization. Further, our results highlight that the relationships among foreign disinformation through social media, social media-induced offline violence, and domestic online media fractionalization in a country are contingent on the government's role in controlling its cyberspace. Implications of our findings to research and practice are discussed. | Varieties of Democracy (V-Dem) databas | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | https://doi.org/10.1016/j.techfore.2021.120661 | |||
44 | Arendt | Alireza Karduni and Isaac Cho and Ryan Wesslen and Sashank Santhanam and Svitlana Volkova and Dustin Arendt and Samira Shaikh and Wenwen Dou | Vulnerable to Misinformation? Verifi! | 2018 | https://arxiv.org/abs/1807.09739 | Working Paper | Cornell University | Identify disinformation | Classifying disinformation | 0 | 1 | 0 | We present Verifi2, a visual analytic system that uses state-of-the-art computational methods to highlight salient features from text, social network, and images. By exploring news on a source level through multiple coordinated views in Verifi2, users can interact with the complex dimensions that characterize misinformation and contrast how real and suspicious news outlets differ on these dimensions | We conduct interviews with experts in digital media, journalism, education, psychology, and computing who study misinformation. Our interviews show promising potential for Verifi2 to serve as an educational tool on misinformation. Furthermore, our interview results highlight the complexity of the problem of combating misinformation and call for more work from the visualization community. | Tweets | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | NA | ||||
45 | Arif | Ahmer Arif and John Robinson and Stephanie Stanek and Elodie Fichet and Paul Townsend and Zena Worku and Kate Starbird | A Closer Look at the Self-Correcting Crowd: Examining Corrections in Online Rumors | 2017 | http://faculty.washington.edu/kstarbi/Arif_Starbird_CorrectiveBehavior_CSCW2017.pdf | Academic Journal | In Proceedings of the ACM 2017 Conference on Computer-Supported Cooperative Work & Social Computing (CSCW '17) | Media consumption | Correcting behaviors in social media | 0 | 1 | 0 | This paper examines how users of social media correct online rumors during crisis events. Focusing on Twitter, we identify different patterns of information correcting behaviors and describe the actions, motivations, rationalizations and experiences of people who exhibited them. To do this, we analyze digital traces across two separate crisis events and interviews of fifteen individuals who generated some of those traces. Salient themes ensuing from this work help us describe: 1) different mechanisms of corrective action with respect to who gets corrected and how; 2) how responsibility is positioned for verifying and correcting information; and 3) how users’ imagined audience influences their corrective strategy. | We synthesize these three components into a preliminary model and explore the role of imagined audiences—both who those audiences are and how they react to and interact with shared information—in shaping users’ decisions about whether and how to correct rumors. | We focus on two false rumors from two crisis events. For both, we captured data using the Twitter Streaming API, executing forward-in-time collections based on keyword search terms selected and curated by our research team. To better understand how Twitter users experience and reflect upon their rumoring and rumor-correcting behaviors, we conducted interviews with people who had participated in one of these rumors. To gain insight into different kinds of user behaviors, we attempted to interview individuals with different types of user behavior signatures. The signatures therefore served as a mechanism for enhancing the diversity of our interview sample | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | USA | NA | ||||
46 | Arif | Ahmer Arif and Leo G. Stewart and Kate Starbird and A. Conrad Nied and Emma S. Spiro | Drawing the Lines of Contention: Networked Frame Contests Within #BlackLivesMatter Discourse | 2017 | https://faculty.washington.edu/kstarbi/Stewart_Starbird_Drawing_the_Lines_of_Contention-final.pdf | Academic Journal | PACM on Human-Computer Interaction | Amplify and create disinformation | Bots behavior | 0 | 1 | 0 | First,their goal is to understand how RU-IRA content was designed to interact with this discourse—which we already understand to be polarized and made up of a heterogenous web of actors who are speaking to different interests and values. Second, it is important to note that the identification and suspension of RU-IRA affiliated accounts is likely part of an evolving and ongoing effort at social media companies. We do not have access to Twitter’s methodology for identifying these accounts, but we do know that at least one of the 2,752 accounts was revealed to be a false positive (i.e. unaffiliated with the Internet Research Agency). Third, their multi-sited research approach—using of Internet Archive data, examining linked websites and considering the activities of these accounts on other social platforms—attempts to address these challenges by acknowledging that information operations on these platforms are interconnected and interrelated activities. | Our empirical findings show how these accounts imitated ordinary users to systematically micro-target different audiences, foster antagonism and undermine trust in information intermediaries. | 58.8M tweets that were posted and collected between December 31st 2015 and October 5th 2016. We collected these tweets by tracking shooting-related keywords like “gun shot”, “gunman”, “shooter” and “shooting” using the Twitter Streaming API. We further filtered this set to tweets containing the terms “BlackLivesMatter”, “BlueLivesMatter”, or “AllLivesMatter” (“*LM”) in the text. The resulting dataset of 248,719 tweets was used in prior work which established divergent and competing frames tied to the #BlackLivesMatter and #BlueLivesMatter hashtags | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | RUS | ||||
47 | Arif | Leo G. Stewart and Ahmer Arif and Kate Starbird | Examining Trolls and Polarization with a Retweet Network | 2017 | https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15603 | Working Paper | ACM WSDM, Workshop on Misinformation and Misbehavior Mining on the Web. | Identify disinformation | Classifying disinformation | 0 | 1 | 0 | This paper offers a systematic lens for exploring the production of “alternative narratives of man-made crisis events” on Twitter, demonstrating how alternative news sites propagate and shape alternative narratives, while mainstream media deny them. Their research methods include mapping a domain network based on users’ tweets, qualitative content analysis and coding of the domains in the dataset, and finally an interpretive analysis to identify patterns, connections, and anomalies in relation to the network graph. | The initial data collection collected tweets that mentioned the following words: shooter, shooting, gunman, gunmen, gunshot, gunshots, shooters, gun shot, gun shots, shootings. The author then narrowed this data set down to Tweets that included keywords indicating an alternative narrative, such as: false flag, falseflag, crisis actor, crisisactor, staged, hoax and “1488.” | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | |||||
48 | Arif | Ahmer Arif and Kelley Shanahan and Fang-Ju Chou and Yoanna Dosouto and Kate Starbird and Emma Spiro | How Information Snowballs: Exploring the Role of Exposure in Online Rumor Propagation | 2016 | http://faculty.washington.edu/kstarbi/Arif_CSCW2016_Snowballs.pdf | Academic Journal | Presented at ACM 2016 Computer Supported Cooperative Work | Identify disinformation | Fake news | 0 | 1 | 0 | In this paper we highlight three distinct approaches to studying rumor dynamics—volume, exposure, and content production. Expanding upon prior work, which has focused on rumor volume, we argue that considering the size of the exposed population is a vital component of understanding rumoring. Additionally, by combining all three approaches we discover subtle features of rumoring behavior that would have been missed by applying each approach in isolation. Using a case study of rumoring on Twitter during a hostage crisis in Sydney, Australia, we apply a mixed-methods framework to explore rumoring and its consequences through these three lenses, focusing on the added dimension of exposure in particular. | Our approach demonstrates the importance of considering both rumor content and the people engaging with rumor content to arrive at a more holistic understanding of communication dynamics. These results have implications for emergency responders and official use of social media during crisis management. | Our research team collected data during the Sydney Siege event for the explicit purpose of examining rumoring behavior during crisis events. Data collection utilized the Twitter Streaming API to track specific event-related terms and phrases including: sydneysiege, sydney, lindt, martinplace, and chocolate shop. Data collection started on December 15 at 11:06am AEDT and ended two weeks later giving an observation window of 14 days over which a total of 5,429,345 tweets were archived for research purposes. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | AUS | NA | ||||
49 | Arif | Ahmer Arif, Leo Graiden Stewart, and Kate Starbird | Acting the Part: Examining Information | 2018 | https://dl.acm.org/doi/10.1145/3274289 | Academic Journal | Proceedings of the ACM on Human-Computer Interaction | Characterize propaganda | Influence the online conversation about the #BlackLivesMatter movement and police-related shootings in the USA during 2016 | 0 | 0 | 0 | "We first present high-level dynamics of this conversation on Twitter using a network graph based on retweet flows that reveals two structurally distinct communities. Next, we identify accounts in this graph that were suspended by Twitter for being affiliated with the Internet Research Agency, an entity accused of conducting information operations in support of Russian political interests. Finally, we conduct an interpretive analysis that consolidates observations about the activities of these accounts." | "Our findings show RU-IRA agents utilizing Twitter and other online platforms to infiltrate politically active online communities. Rather than transgressing community norms, these accounts undertook efforts to connect to the cultural narratives, stereotypes, and political positions of their imagined audiences. Understanding this performative aspect of RU-IRA accounts is critical for understanding how the work of information operations not only includes activities of disseminating true or false information on social media, but also activities to reflect and shape the performances of other (not RU-affiliated) actors in these communities." | "Our initial dataset consisted of 58.8M tweets that were posted and collected between December 31st 2015 and October 5th 2016. We collected these tweets by tracking shooting-related keywords like “gun shot”, “gunman”, “shooter” and “shooting” using the Twitter Streaming API." | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | USA | RUS | https://doi.org/10.1145/3274289 | |||
50 | Arkoudas | Luca Buccoliero and Elena Bellio and Giulia Crestini and Alessandra Arkoudas | Twitter and politics: Evidence from the US presidential elections 2016 | 2018 | https://www.tandfonline.com/doi/abs/10.1080/13527266.2018.1504228 | Academic Journal | Journal of Marketing Communications | Media consumption | Behavior of Hillary Clinton and Donald Trump at Twitter | 0 | 1 | 0 | The more candidates used Twitter to broadcast their thoughts, the more people retweeted them spreading their messages and journalists mentioned tweets in their election coverage creating a virtuous circle that brought more and more attention to the micro-blogging platform. This article analyzes the tweets of Hillary Clinton and Donald Trump in order to understand the communication strategies performed through this media. | As of July 2018, President Trump has over 53 million followers and is one of the most followed account on the platform. It is just evidential that he is still a ‘tweeting’ President: from the 2016 Election to July 2018 he tweeted using his personal account around 4300 times, with an average of 7 tweets per day dealing with any topic of local and global relevance and with personal matters. It would be interesting to implement further research to evaluate the actual use of Twitter done by the US President in order to see if his attitude has remained the same as the one during the campaign or if differences are registered. Furthermore, Twitter’s unique data structure allows researchers to document the behavior of Twitter users and these results could be combined with ad-hoc surveys. Also experiments could be built and implemented to identify mechanisms of political uses of Twitter. This holds opportunities for future research on the potential of political Twitter messages to move their recipients into action. | Tweets from Hillary Clinton and Donald Trump. Trump has been active on Twitter since March 2009 while Clinton since April 2013. At the end of November 2016, Trump had 16.3 million followers and Clinton had 11.4 million. Both candidates had a consistent growth of their followers’ in the last 6 months, respectively +73.4% for Trump and 59.3% for Clinton. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | RUS | ||||
51 | Aro | Jessikka Aro | The Cyberspace War: Propaganda and Trolling as Warfare Tools | 2016 | https://journals.sagepub.com/doi/full/10.1007/s12290-016-0395-5 | Academic Journal | European view | Identify disinformation | Roles and strategy of trolls | 0 | 1 | 0 | My investigation has discovered that coordinated social media propaganda writers are twisting and manipulating the public debate in Finland. Trolls and bots distribute vast amounts of false information in various languages, and target individual citizens for aggressive operations. Aggressive trolls have created a feeling of fear among some of my interviewees, causing them to stop making Russiarelated comments online. | Trolling has had a serious impact on freedom of speech, even outside Russia. Thus, it should be viewed as a national security threat that needs to be addressed accordingly. The question is: how should the Kremlin’s trolls and disinformation be countered? | Study cases | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | NA | RUS | ||||
52 | Åsberg | Martin Kragh and Sebastian Åsberg | Russia’s strategy for influence through public diplomacy and active measures: the Swedish case | 2017 | https://www.tandfonline.com/doi/full/10.1080/01402390.2016.1273830 | Academic Journal | Journal of Strategic Studies | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | Russia, as many contemporary states, takes public diplomacy seriously. Since the inception of its English language TV network Russia Today in 2005 (now ‘RT’), the Russian government has broadened its operations to include Sputnik news websites in several languages and social media activities. Moscow, however, has also been accused of engaging in covert influence activities – behaviour historically referred to as ‘active measures’ in the Soviet KGB lexicon on political warfare. In this paper, we provide empirical evidence on how Russia since 2014 has moved towards a preference for active measures towards Sweden, a small country in a geopolitically important European region. We analyse the blurring of boundaries between public diplomacy and active measures; document phenomena such as forgeries, disinformation, military threats and agents of influence and define Russian foreign policy strategy. | In summary, we conclude that the overarching goal of Russian policy towards Sweden and the wider Baltic Sea is to preserve the geostrategic status quo, which is identified with a security order minimising NATO presence in the region. | Timeline of 26 forgeries and fake articles appearing in the Swedish information climate, 2015–July 2016. | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | SWE | RUS | ||||
53 | Atanasov | Atanas Atanasov, Gianmarco De Francisci Morales amd Preslav Nakov | Predicting the role of political trolls in social media | 2019 | https://arxiv.org/pdf/1910.02001.pdf | working Paper | Cornell University | Identify disinformation | Influence political discourse | 0 | 1 | 0 | We investigate the political roles of "Internet trolls" in social media. Political trolls, such as the ones linked to the Russian Internet Research Agency (IRA), have recently gained enormous attention for their ability to sway public opinion and even influence elections. Analysis of the online traces of trolls has shown different behavioral patterns, which target different slices of the population. However, this analysis is manual and labor-intensive, thus making it impractical as a first-response tool for newly-discovered troll farms | In this paper, we show how to automate this analysis by using machine learning in a realistic setting. In particular, we show how to classify trolls according to their political role ---left, news feed, right--- by using features extracted from social media, i.e., Twitter, in two scenarios: (i) in a traditional supervised learning scenario, where labels for trolls are available, and (ii) in a distant supervision scenario, where labels for trolls are not available, and we rely on more-commonly-available labels for news outlets mentioned by the trolls. Technically, we leverage the community structure and the text of the messages in the online social network of trolls represented as a graph, from which we extract several types of learned representations, i.e.,~embeddings, for the trolls. Experiments on the "IRA Russian Troll" dataset show that our methodology improves over the state-of-the-art in the first scenario, while providing a compelling case for the second scenario, which has not been explored in the literature thus far. | Twitter IRA data | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | RUS | ||||
54 | Atluri | Ussama Yaqub and Nitesh Sharma and Rachit Pabreja and Soon Ae Chun and Vijayalakshmi Atluri and Jaideep Vaidya | Analysis and visualization of subjectivity and polarity of Twitter location data | 2018 | https://dl.acm.org/purchase.cfm?id=3209313 | Academic Journal | Proceedings of the 19th Annual International Conference on Digital Government Research: Governance in the Data Age | Media consumption | Visualize polarization | 0 | 1 | 0 | First, it studies the subjectivity and polarity of Twitter data based on the location of the tweets. Second, we present a web-based system which can enable in the collection of Twitter data, extraction of the location, perform data analysis and then to plot them on a maps for visualization | Findings from this research will enable further fine grained analysis of Twitter data by using the location variable, allowing for more accurate results of user sentiment and behavior analysis | We employ US presidential election data of 2016 as a case study to test our research questions and to demonstrate our results utilizing this web-based system. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | NA | ||||
55 | Atluri | Ussama Yaqub and Soon Ae Chun and Vijayalakshmi Atluri and Jaideep Vaidya | Analysis of political discourse on twitter in the context of the 2016 US presidential elections | 2017 | https://www.sciencedirect.com/science/article/pii/S0740624X17301910 | Academic Journal | Government Information Quarterly | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | We investigated the sentiment of tweets by the two main presidential candidates, Hillary Clinton and Donald Trump, along with almost 2.9 million tweets by Twitter users during the 2016 US Presidential Elections. We analyzed these short texts to evaluate how accurately Twitter represented the public opinion and real world events of significance related with the elections. We also analyzed the behavior of over a million distinct Twitter users to identify whether the platform was used to share original opinions and to interact with other users or whether few opinions were repeated over and over again with little inter-user dialogue. Finally, we wanted to assess the sentiment of tweets by both candidates and their impact on the election related discourse on Twitter. | Some of our findings included the discovery that little original content was created by users and Twitter was primarily used for rebroadcasting already present opinions in the form of retweets with little communication between users. Also of significance was the finding that sentiment and topics expressed on Twitter can be a good proxy of public opinion and important election related events. Moreover, we found that Donald Trump offered a more optimistic and positive campaign message than Hillary Clinton and enjoyed better sentiment when mentioned in messages by Twitter users. | Tweets | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | NA | ||||
56 | Atodiresei | Costel-Sergiu Atodiresei and Alexandru Tănăselea and Adrian Iftene | Identifying Fake News and Fake Users on Twitter | 2018 | https://www.sciencedirect.com/science/article/pii/S1877050918312559 | Academic Journal | Procedia Computer Science | Identify disinformation | Detecting fake news | 0 | 1 | 0 | ThereisaTwittercrawlercomponent,whichcollects tweets and adds them to our database. When we will need tweets from trustworthy sources to compare with our current one, we can retrieve them directly from our database. The Processing module: when a user wants to know the credibility of a new tweet, he inputs the link of the tweet in our interface. Our algorithm then uses an NER (Named EntityRecognition)component,whichsplitthetextintoitscomposingparts:itbringsouttheentities(generally,nounsandtheirrelativeimportanceinthecontext),thetopics,thesocialtags,theoveralltweetsentimentandthehashtag sentiment. | Until now, the presented project has reached its goal, meaning that based on tweet’s text and tweet’s user, it returns asetofstatisticsaboutthetweet’sveracity.Theprojectisstillunderdevelopingandwestillworkonpresentedmodules in order to improve the quality of everyone and the entire overall quality of the system.Detecting indeed whether a news is fake or not, based solely on its popularity in the same social network might not be the best idea. In present, Facebook is using human resources that investigate the popular posts in order to decide if they are fake or not. | Tweets and fake news | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | USA | NA | ||||
57 | Attar | Sushil Shelke and Vahida Attar | Source detection of rumor in social network – A review | 2019 | https://www.sciencedirect.com/science/article/pii/S2468696418300934 | Academic Journal | Online Social Networks and Media | Identify disinformation | Literature review | 0 | 0 | 1 | Most of the existing reviews which focused on source detection relate to various application domains and network perspective. But as per the need of current social networking usage and its influence on the society, it is a crucial and important topic to review the source detection approaches in the social network. The objective of this paper is to study and analyze the source detection approaches of rumor or misinformation in a social network. As an outcome of the literature study, we present the pictorial taxonomy of factors to be considered for the source detection approach and the classification of current source detection approaches in the social network. | The focus has been given to various state-of-the-art source detection approaches of rumor or misinformation and comparison between approaches in social networks. This paper also focused on research challenges in current source detection approaches, public datasets and future research directions. | The Synthetic datasets are mainly structured in terms of tree and graph. The Tree networks are represented by Random d-regular tree. Small-world (SW) networks and scale free networks are basically used for graph networ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | ||||
58 | Avram | Mihai Avram, Nicholas Micallef, Sameer Patil, Filippo Menczer | Exposure to Social Engagement Metrics Increases Vulnerability to Misinformation | 2020 | https://arxiv.org/abs/2005.04682 | Academic Journal | Harvard Kennedy School Misinformation Review | Media consumption | Combating fake news | 0 | 1 | 0 | Playing the game Fakey which "simulates fact checking on a social media feed" | "News feeds in virtually all social media platforms include engagement metrics, such as the number of times each post is liked and shared. We find that exposure to these social engagement signals increases the vulnerability of users to misinformation." | Researchers compiled data from users playing the game Fakey. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ||||||
59 | Azaria | VS Subrahmanian and Amos Azaria and Skylar Durst and Vadim Kagan and Aram Galstyan and Kristina Lerman and Linhong Zhu and Emilio Ferrara and Alessandro Flammini and Filippo Menczer | The darpa twitter bot challenge | 2016 | https://arxiv.org/ftp/arxiv/papers/1601/1601.05140.pdf | Academic Journal | IEEE Computer | Amplify and create disinformation | Bots behavior | 0 | 1 | 0 | Past work regarding influence bots often has difficulty supporting claims about accuracy,since there is limited ground truth (though some exceptions do exist [3,7]). However, with the exceptionof [3], no past work has looked specifically at identifying influence bots on a specific topic | This paper describes the DARPA Challenge and describes the methods used by the three top-ranked teams. | DARPA held a 4-week competition in February/March 2015 in which multiple teams supported by the DARPA Social Media in Strategic Communications program competedto identify a set of previously identified “influence bots” serving as ground truth on a specific topicwithin Twitter | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | ||||
60 | Azzimonti | Marina Azzimonti and Marcos Fernandes | Social Media Networks, Fake News, and Polarization | 2018 | https://www.nber.org/papers/w24462 | Working Paper | NBER | Amplify and create disinformation | Bots behavior | 0 | 1 | 0 | We study how the structure of social media networks and the presence of fake news might affect the degree of misinformation and polarization in a society. For that, we analyze a dynamic model of opinion exchange in which individuals have imperfect information about the true state of the world and are partially bounded rational. Key to the analysis is the presence of internet bots: agents in the network that do not follow other agents and are seeded with a constant flow of biased information. We characterize how the flow of opinions evolves over time and evaluate the determinants of long-run disagreement among individuals in the network. To that end, we create a large set of heterogeneous random graphs and simulate a long information exchange process to quantify how the bots’ ability to spread fake news and the number and degree of centrality of agents susceptible to them affect misinformation and polarization in the long-run. | We find that when bots at one extreme are relatively more e�cient at manipulating news (by targeting a small number of influential agents), the may be able to generate full misinformation in the long run, where beliefs are at one end of the political spectrum. There would be no polarization in that case, but at the expense of agents converging to the wrong value of θ, the parameter of interest. There are other situations where agents are on average correct, but have nonetheless very heterogeneous opinions. These cases would still be sub-optimal, as they may result in ine�cient gridlock and inaction | We fix the number of agents (or nodes) to n = 37 and the true state of the world at θ = 0.5. We also fix the initial distribution of beliefs so that the same mass of the total population lies in the middle point of each one of 7 groups. This rule basically distributes our agents evenly over the political spectrum [0, 1] such that each of the 7 groups contains exactly 7 of the total mass of agents. Moreover, we set the same variance for each agent world-view to be σ2 = 0.03. With both opinion and variance, we are able to compute the initial parameter vector (α0 , β0).Given these parameters, we draw and populate a large number M = 8, 248 of initial random networks g0 following the BAUER model described in Section (4.1). To generate heterogeneity, we consider values for m (number of meetings) in the set {1, .., 5}, the preferential attachment α in [0.5, 1.5], the attractiveness of nodes with no adjacent edges a ∈ {1, ...4}, and the probability p ∈ [0.01, 0.1] | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | NA | NA | ||||
61 | Bachl | Marko Bachl | An Evaluation of Retrospective Facebook Content Collection | 2018 | https://osf.io/6txge | Working Paper | University of Hohenheim | Characterize propaganda | Report propaganda | 0 | 1 | 0 | The present evaluation study aims to assess whether—and if so, how—problematic this common practice is in terms of unavailable content. I investigated whether a sample of content items, which were posted to 408 German political Facebook pages in March 2017, could be retrospectively collected in June 2017, September 2017, and March 2018. 27% of the 132,068 test items were no longer accessible after 12 months. The deletion of complete pages was rare, but some prominent pages were not available retrospectively. Posts by the pages themselves were far more likely to be available compared to content, which was posted by private accounts to the pages. There were only small differences between the political parties, but there was substantial variation between the distinct pages. | I conclude with a discussion of implications for single research projects (applying timely data collection whenever possible) and for communication research and related disciplines (considering institutionalized data collection efforts). | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | GER | UNK | |||||
62 | Badawy | Adam Badawy, Emilio Ferrara, Kristina Lerman | Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign | 2018 | https://ieeexplore.ieee.org/document/8508646 | Academic Journal | International Conference on Advances in Social Networks Analysis and Mining (ASONAM) | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | we explore the effects of this manipulation campaign, taking a closer look at users who re-shared the posts produced on Twitter by the Russian troll accounts publicly disclosed by U.S. Congress investigation. We collected a dataset with over 43 million elections-related posts shared on Twitter between September 16 and November 9, 2016 by about 5.7 million distinct users | Conservatives retweeted Russian trolls significantly more often than liberals and produced 36 times more tweets. Additionally, most of the troll content originated in, and was shared by users from Southern states. Using state-ofthe-art bot detection techniques, we estimated that about 4.9% and 6.2% of liberal and conservative users respectively were bots. Text analysis on the content shared by trolls reveals that they had a mostly conservative, pro-Trump agenda. Although an ideologically broad swath of Twitter users were exposed to Russian trolls in the period leading up to the 2016 U.S. Presidential election | We collected a dataset with over 43 million elections-related posts shared on Twitter between September 16 and November 9, 2016 by about 5.7 million distinct users | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | RUS | ||||
63 | Badawy | Luca Luceri, Ashok Deb, Adam Badawy and Emilio Ferrara | Red Bots Do It Better:Comparative Analysis of Social Bot Partisan Behavior | 2019 | https://dl.acm.org/doi/abs/10.1145/3308560.3316735 | Academic Journal | Companion Proceedings of The 2019 World Wide Web Conference | Amplify and create disinformation | Bots behavior | 0 | 1 | 0 | Recent research brought awareness of the issue of bots on social media and the significant risks of mass manipulation of public opinion in the context of political discussion. In this work, we leverage Twitter to study the discourse during the 2018 US midterm elections and analyze social bot activity and interactions with humans. We collected 2.6 million tweets for 42 days around the election day from nearly 1 million users. We use the collected tweets to answer three research questions: (i) Do social bots lean and behave according to a political ideology? (ii) Can we observe different strategies among liberal and conservative bots? (iii) How effective are bot strategies in engaging humans? | We show that social bots can be accurately classified according to their political leaning and behave accordingly. Conservative bots share most of the topics of discussion with their human counterparts, while liberal bots show less overlap and a more inflammatory attitude. We studied bot interactions with humans and observed different strategies. Finally, we measured bots embeddedness in the social network and the extent of human engagement with each group of bots. Results show that conservative bots are more deeply embedded in the social network and more effective than liberal bots at exerting influence on humans. | In this study, we use Twitter to investigate the partisan behavior of malicious accounts during the 2018 US midterm elections. For this purpose, we carried out a data collection from the month prior (October 6, 2018) to two weeks after (November 19, 2018) the day of the election. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | USA | ||||
64 | Badawy | Samar Haider, Luca Luceri, Ashok Deb, Adam Badawy, Nanyun Peng, and Emilio Ferrara | Detecting Social Media Manipulation in Low-Resource Languages | 2020 | https://arxiv.org/abs/2011.05367 | Working Paper | Cornell University | Identify disinformation | Trolls and bots behaviour | 0 | 1 | 0 | We investigate whether and to what extent malicious actors can be detected in low-resource language settings. We discovered that a high number of accounts posting in Tagalog were suspended as part of Twitter's crackdown on interference operations after the 2016 US Presidential election. By combining text embedding and transfer learning, our framework can detect, with promising accuracy, malicious users posting in Tagalog without any prior knowledge or training on malicious content in that language | We first learn an embedding model for each language, namely a high-resource language (English) and a low-resource one (Tagalog), independently. Then, we learn a mapping between the two latent spaces to transfer the detection model. We demonstrate that the proposed approach significantly outperforms state-of-the-art models, including BERT, and yields marked advantages in settings with very limited training data-the norm when dealing with detecting malicious activity in online platforms. | we use Twitter as a test-bed to detect the activity of malicious accounts focusing on the 2016 US presidential election. Tweets were collected through the Twitter Streaming API using 23 election keywords (5 for Donald Trump, 4 for Hillary Clinton, 3 for third-party candidates, and 11 for the general election terms) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | ||||
65 | Badrinathan | Sumitra Badrinathan | Educative Interventions to Combat Misinformation: Evidence from a Field Experiment in India | 2020 | https://www.cambridge.org/core/journals/american-political-science-review/article/abs/educative-interventions-to-combat-misinformation-evidence-from-a-field-experiment-in-india/A522EB5164406DE320647014946D31B3 | Academic Journal | American Political Science Review | Characterize propaganda | Combating fake news | 0 | 1 | 0 | This study uses a field experiment in India to test the efficacy of a pedagogical intervention on respondents’ ability to identify misinformation during the 2019 elections (N = 1,224). Treated respondents received hour-long in-person media literacy training in which enumerators discussed inoculation strategies, corrections, and the importance of verifying misinformation, all in a coherent learning module. | This study uses a field experiment in India to test the efficacy of a pedagogical intervention on respondents’ ability to identify misinformation during the 2019 elections (N = 1,224). Treated respondents received hour-long in-person media literacy training in which enumerators discussed inoculation strategies, corrections, and the importance of verifying misinformation, all in a coherent learning module. | Respondents were selected through a random walk procedure. Within the sampling area, a random sample of polling booths (smallest administrative units) were selected to serve as enumeration areas. Within each enumeration area, enumerators were instructed to survey 10–12 households following a random walk procedure. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | IND | NA | ||||
66 | Bagchi | Rudra M Tripathy and Amitabha Bagchi and Sameep Mehta. | A Study of Rumor Control Strategies on Social Networks | 2010 | http://www.cse.iitd.ac.in/~bagchi/p1817-tripathy.pdf | Working Paper | International Conference on Information and Knowledge Management. ACM | Identify disinformation | Fake news | 0 | 1 | 0 | We model rumor spread as a diffusion process on a network and suggest the use of an “anti-rumor” process similar to the rumor process. We study two natural models by which these anti-rumors may arise. The main metrics we study are the belief time, i.e., the duration for which a person believes the rumor to be true and point of decline, i.e., point after which anti-rumor process dominates the rumor process. We evaluate our methods by simulating rumor spread and anti-rumor spread on a data set derived from the social networking site Twitter and on a synthetic network generated according to the Watts and Strogatz model | We find that the lifetime of a rumor increases if the delay in detecting it increases, and the relationship is at least linear. Further our findings show that coupling the detection and anti-rumor strategy by embedding agents in the network, we call them beacons, is an effective means of fighting the spread of rumor, even if these beacons do not share information. | Twitter and on a synthetic network generated according to the Watts and Strogatz model. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | ||||
67 | Bail | Christopher A. Bail, Brian Guay, Emily Maloney, Aidan Combs, D. Sunshine Hillygus, Friedolin Merhout, Deen Freelon, and Alexander Volfovsky | Assessing the Russian Internet Research Agency’s impact on the political attitudes and behaviors of American Twitter users in late 2017 | 2020 | https://www.pnas.org/content/117/1/243/ | Academic Journal | PNSA | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | While numerous studies analyze the strategy of online influence campaigns, their impact on the public remains an open question. We investigate this question combining longitudinal data on 1,239 Republicans and Democrats from late 2017 with data on Twitter accounts operated by the Russian Internet Research Agency. | We find no evidence that interacting with these accounts substantially impacted 6 political attitudes and behaviors. Descriptively, interactions with trolls were most common among individuals who use Twitter frequently, have strong social-media “echo chambers,” and high interest in politics. These results suggest Americans may not be easily susceptible to online influence campaigns, but leave unanswered important questions about the impact of Russia’s campaign on misinformation, political discourse, and 2016 presidential election campaign dynamics. | We investigate this question combining longitudinal data on 1,239 Republicans and Democrats from late 2017 with data on Twitter accounts operated by the Russian Internet Research Agency | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | RUS | ||||
68 | Bakir | Vian Bakir and Andrew McStay | Fake News and The Economy of Emotions | 2017 | https://www.tandfonline.com/doi/abs/10.1080/21670811.2017.1345645 | Academic Journal | Digital Journalism | Media consumption | Fake news rating | 0 | 1 | 0 | This paper examines the 2016 US presidential election campaign to identify problems with, causes of and solutions to the contemporary fake news phenomenon. To achieve this, we employ textual analysis and feedback from engagement, meetings and panels with technologists, journalists, editors, non-profits, public relations firms, analytics firms and academics during the globally leading technology conference, South-by-South West, in March 2017. | We further argue that what is most significant about the contemporary fake news furore is what it portends: the use of personally and emotionally targeted news produced by algo-journalism and what we term “empathic media”. In assessing solutions to this democratically problematic situation, we recommend that greater attention is paid to the role of digital advertising in causing, and combating, both the contemporary fake news phenomenon, and the near-horizon variant of empathically optimised automated fake news. | Fake news. Facebook | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | USA | NA | ||||
69 | Bakir | Genevieve Gorrell, Mehmet E. Bakir, Ian Roberts, Mark A. Greenwood, Benedetta Iavarone and Kalina Bontcheva | Partisanship, Propaganda and Post-Truth Politics: Quantifying Impact in Online Debate | 2019 | https://arxiv.org/pdf/1902.01752.pdf | Working Paper | University of Sheffield | Media consumption | Report propaganda | 0 | 1 | 0 | This paper is focused on studying the role of politically-motivated actors and their strategies for influencing and manipulating public opinion online: partisan media, state-backed propaganda, and post-truth politics. In particular, we present quantitative research on the presence and impact of these three “Ps” in online Twitter debates in two contexts: (i) the run up to the UK EU membership referendum (“Brexit”); and (ii) the information operations of Russia-backed online troll accounts. We first compare the impact of highly partisan versus mainstream media during the Brexit referendum, specifically comparing tweets by half a million “leave” and “remain” supporters. Next, online propaganda strategies are examined, specifically left- and right-wing troll accounts. Lastly, we study the impact of misleading claims made by the political leaders of the leave and remain campaigns. This is then compared to the impact of the Russia-backed partisan media and propaganda accounts during the referendum | In particular, just two of the many misleading claims made by politicians during the referendum were found to be cited in 4.6 times more tweets than the 7,103 tweets related to Russia Today and Sputnik and in 10.2 times more tweets than the 3,200 Brexit-related tweets by the Russian troll accounts | Around 17.5 million tweets were collected up to and including 23 June 2016 (EU referendum day). The highest volume was 2 million tweets on Jun 23rd (only 3,300 lost due to rate limiting), with just over 1.5 million during poll opening times | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | GBR | GBR | ||||
70 | Bakshy | Eytan Bakshy and Solomon Messing | Exposure to ideologically diverse news and opinion on Facebook | 2015 | https://www.researchgate.net/profile/Solomon_Messing/publication/276067921_Political_science_Exposure_to_ideologically_diverse_news_and_opinion_on_Facebook/links/5699070a08ae6169e55161f5/Political-science-Exposure-to-ideologically-diverse-news-and-opinion-on-Facebook.pdf | Academic Journal | Science | Media consumption | Ideological homophily in friend networks | 0 | 1 | 0 | We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook’s algorithmically ranked News Feed and further studied users’ choices to click through to ideologically discordant content. | Political disagreement on social media appears to be high | 10.1 million U.S. Facebook users | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | ||||
71 | Bakshy | Jason J. Jones and Robert M. Bond and Eytan Bakshy and Dean Eckles and James H. Fowler | Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 U.S. presidential election | 2017 | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0173851 | Academic Journal | PloS one | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | A large-scale experiment during the 2010 U.S. Congressional Election demonstrated a positive effect of an online get-out-the-vote message on real world voting behavior. Here, we report results from a replication of the experiment conducted during the U.S. Presidential Election in 2012. | In spite of the fact that get-out-the-vote messages typically yield smaller effects during high-stakes elections due to saturation of mobilization efforts from many sources, a significant increase in voting was again observed. Voting also increased significantly among the close friends of those who received the message to go to the polls, and the total effect on the friends was likely larger than the direct effect, suggesting that understanding social influence effects is potentially even more important than understanding the direct effects of messaging. These results replicate earlier work and they add to growing evidence that online social networks can be instrumental for spreading offline behaviors. | Facebook. Randomized get-out-the-vote (GOTV) messages to 61 million Facebook users, 6 million of whom were matched to publicly available voter registration records | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | ||||
72 | Bandaway | Adam Badaway and Kristan Lerman and Emilio Ferrara | Who Falls for Online Political Manipulation? | 2019 | https://dl.acm.org/citation.cfm?id=3316494 | Academic Journal | In Companion Proceedings of The 2019 World Wide Web Conference | Amplify and create disinformation | How disinformation is amplified | 0 | 1 | 0 | Our aim is twofold: first, we test whether predicting users who spread trolls’ content is feasible in order to gain insight on how to contain their influence in the future; second, we identify features that are most predictive of users who either intentionally or unintentionally play a vital role in spreading this malicious content. | We show that political ideology, bot likelihood scores, and some activity-related account meta data are the most predictive features of whether a user spreads trolls’ content or not | Twitter Dataset: 43 million elections-related posts shared on Twitter between September 16 and November 9, 2016, by about 5.7 million users | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | RUS | ||||
73 | Bandeira | Luiza Bandeira, Donara Barojan, Roberta Braga, Jose Luis Peñarredonda, Maria Fernanda Pérez Argüello | Disinformation in Democracies: Strengthening Digital Resilience in Latin America | 2019 | https://www.atlanticcouncil.org/publications/reports/disinformation-democracies-strengthening-digital-resilience-latin-america | Report | Atlantic Coucil | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 0 | 1 | "Arapidlyevolvinginformationenvironment— wherein innovation often outpaces traditional security measures and governance at the private and public levels—became a catalyst and a vector for the spread ofrumorsandfalseinformation.Disinformation actors, whether through organic disinformation or by employing artificial amplification, provoked fear and anxiety, and sought to illicitly influence voters, under- mining the electoral process along the way." "Toaddressthiscomplexsetofchallenges,the AtlanticCouncil’sAdrienneArshtLatinAmerica CenteranditsDigitalForensicResearchLab (DFRLab) partnered with local organizations to iden- tify, expose, and explain disinformation, to promote increased dialogue, to support concerted action, and to increase digital and media literacy as a bulwark of democracy. #ElectionWatch Latin America—as the effort was called—shed new light on the ways disinformation, misinformation, and automation appeared within the context of each country’s election environments and influenced outcomes." | "In Brazil, Atlantic Council research conducted in real time found that disinformation comprised primarily organic disinformation—driven by polarization and a lack of trust in institutions. In Colombia, the Atlantic Councilobservedasimilartrend,exacerbatedat times by political leaders and the media’s purposeful or accidental spread of false information. In Mexico, the Council found automation and artificial amplifica- tion to be more prominent. Atlantic Council research- ers uncovered actors who hired commercial bots for financial gain and used political bots for the spread of specific electoral messages. At the state level, meanwhile, disinformation about the electoral process exacerbated polarization." | Study cases | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | BRA, COL, MEX | NA | ||||
74 | Bankston | Young Mie Kim and Jordan Hsu and David Neiman and Colin Kou and Levi Bankston and Soo Yun Kim and Richard Heinrich and Robyn Baragwanath and Garvesh Raskutti | The Stealth Media? Groups and Targets behind Divisive Issue Campaigns on Facebook | 2018 | https://www.tandfonline.com/doi/abs/10.1080/10584609.2018.1476425 | Academic Journal | Political Communication | Characterize propaganda | Characteristics of IRA propaganda | 0 | 1 | 0 | In light of the foreign interference in the 2016 U.S. elections, the present research asks the question of whether the digital media has become the stealth media for anonymous political campaigns. By utilizing a user-based, real-time, digital ad tracking tool, the present research reverse engineers and tracks the groups (Study 1) and the targets (Study 2) of divisive issue campaigns based on 5 million paid ads on Facebook exposed to 9,519 individuals between September 28, 2016, and November 8, 2016. | he findings reveal groups that did not file reports to the Federal Election Commission (FEC)—nonprofits, astroturf/movement groups, and unidentifiable “suspicious” groups, including foreign entities—ran most of the divisive issue campaigns. One out of six suspicious groups later turned out to be Russian groups. The volume of ads sponsored by non-FEC groups was 4 times larger than that of FEC groups. Divisive issue campaigns clearly targeted battleground states, including Pennsylvania and Wisconsin where traditional Democratic strongholds supported Donald Trump by a razor-thin margin. The present research asserts that media ecology, the technological features and capacity of digital media, as well as regulatory loopholes created by Citizens United v. FEC and the FEC’s disclaimer exemption for digital platforms contribute to the prevalence of anonymous groups’ divisive issue campaigns on digital media. The present research offers insight relevant for regulatory policy discussion and discusses the normative implications of the findings for the functioning of democracy. | Survey data from Escope | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | NA | NA | ||||
75 | Baptista | João Pedro Baptista and Anabela Gradim | Online disinformation on Facebook: the spread of fake news during the Portuguese 2019 election | 2020 | https://www.tandfonline.com/doi/pdf/10.1080/14782804.2020.1843415?casa_token=xlfFJgA0V9MAAAAA:rF2A-VichdhnL19WkCzIeUJSbguqWnsI9tttp9GpS885s7qm7YV6EyYW2GBIf7zKdrtsO4gfalAZWA | Academic Journal | Journal of Contemporary European Studies | Amplify and create disinformation | Fake news | 0 | 1 | 0 | This study focused on the Portuguese 2019 elections to assess the reach of fake news compared to mainstream media news and to verify whether fake news had specific targets. We reviewed all posts (N=1197) from newspaper Facebook pages and fake news Facebook pages published during the campaign to verify their engagement. BuzzSumo assessed popularity by counting all posts’ shares, reactions and comments. Iramuteq software related the content of all published headlines by analyzing clusters of the most frequent words. | Findings show that fake news had no greater reach than real news during the election campaign. However, fake news are more likely to be shared, while real news tend to get more reactions and more comments. In fake news headlines the terms associated with left-wing and government are the most common. The prime minister and the Socialist Party are associated with negative connotations. Results suggest that Portuguese fake news are related to rightwing extremism and publish hate content targeting corruption and leftist policies in general. Unlike other countries, anti-immigration discourse and fearmongering were not prominent contents. | To evaluate the reach and effect of fake news pages on Facebook compared to newspapers, we considered for our analysis all posts, with political headlines, shared during this period by both websites. We used the BUZZSUMO database (buzzsumo.com), a platform that collects the engagement of news articles on social media, counting all the shares, reactions and comments from each post. In total we analyzed the engagement of 1197 posts (1020 from newspaper Facebook pages and 177 from fake news pages on Facebook). | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | ||||
76 | Bar | Jieun Shin and Lian Jian and Kevin Driscoll and Francois Bar | The diffusion of misinformation on social media: Temporal pattern, message, and source | 2018 | https://www.sciencedirect.com/science/article/pii/S0747563218300669 | Academic Journal | Computers in Human Behavior | Amplify and create disinformation | Diffusion of disinformation | 0 | 1 | 0 | This study examines dynamic communication processes of political misinformation on social media focusing on three components: the temporal pattern, content mutation, and sources of misinformation. Using text analysis based on time series. | We found that while false rumors (misinformation) tend to come back multiple times after the initial publication, true rumors (facts) do not. Rumor resurgence continues, often accompanying textual changes, until the tension around the target dissolves. We observed that rumors resurface by partisan news websites that repackage the old rumor into news and, gain visibility by influential Twitter users who introduce such rumor into the Twittersphere. In this paper, we argue that media scholars should consider the mutability of diffusing information, temporal recurrence of such messages, and the mechanism by which these messages evolve over time. | We traced the lifecycle of 17 popular political rumors that circulated on Twitter over 13 months during the 2012 U.S. presidential election. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | NA | ||||
77 | Baragwanath | Young Mie Kim, Jordan Hsu, David Neiman, Colin Kou, Levi Bankston, Soo Yun Kim, Richard Heinrich, Robyn Baragwanath, Garvesh Raskutti | The Stealth Media? Groups and Targets behind Divisive Issue Campaigns on Facebook | 2018 | https://www.tandfonline.com/doi/pdf/10.1080/10584609.2018.1476425 | Academic Journal | Political Communication | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | In light of the foreign interference in the 2016 U.S. elections, the present research asks the question of whether the digital media has become the stealth media for anonymous political campaigns. By utilizing a user-based, real-time, digital ad tracking tool, the present research reverse engineers and tracks the groups (Study 1) and the targets (Study 2) of divisive issue campaigns based on 5 million paid ads on Facebook exposed to 9,519 individuals between September 28, 2016, and November 8, 2016 | The findings reveal groups that did not file reports to the Federal Election Commission (FEC)—nonprofits, astroturf/movement groups, and unidentifiable “suspicious” groups, including foreign entities—ran most of the divisive issue campaigns. One out of six suspicious groups later turned out to be Russian groups. The volume of ads sponsored by non-FEC groups was 4 times larger than that of FEC groups. Divisive issue campaigns clearly targeted battleground states, including Pennsylvania and Wisconsin where traditional Democratic strongholds supported Donald Trump by a razor-thin margin. The present research asserts that media ecology, the technological features and capacity of digital media, as well as regulatory loopholes created by Citizens United v. FEC and the FEC’s disclaimer exemption for digital platforms contribute to the prevalence of anonymous groups’ divisive issue campaigns on digital media | By utilizing a user-based, real-time, digital ad tracking tool, the present research reverse engineers and tracks the groups (Study 1) and the targets (Study 2) of divisive issue campaigns based on 5 million paid ads on Facebook exposed to 9,519 individuals between September 28, 2016, and November 8, 2016 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | USA | RUS | ||||
78 | Barash | Vlad Barash and Matthew Hindman | Disinformation fake news and influence campaigns on Twitter | 2018 | https://kf-site-production.s3.amazonaws.com/media_elements/files/000/000/238/original/KF-DisinformationReport-final2.pdf | Report | Knight Foundation | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | Tools and mapping methods from Graphical, a social media intelligence firm | Consistent with other research, we find more than 6.6 million tweets linking to fake and conspiracy news publishers in the month before the 2016 election. Yet disinformation continues to be a substantial problem postelection, with 4.0 million tweets linking to fake and conspiracy news publishers found in a 30-day period from mid-March to mid-April 2017. Contrary to claims that fake news is a game of “whack-a-mole,” more than 80 percent of the disinformation accounts in our election maps are still active as this report goes to press. These accounts continue to publish more than a million tweets in a typical day. | 10 million tweets from 700,000 Twitter accounts that linked to more than 600 fake and conspiracy news outlets | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | NA | ||||
79 | Barash | John D. Gallacher and Vlad Barash and Philip N. Howard and John Kelly | Junk News on Military Affairs and National Security: Social Media Disinformation Campaigns Against US Military Personnel and Veterans | 2017 | https://comprop.oii.ox.ac.uk/research/working-papers/vetops/ | Working Paper | Computational Propaganda Research Project | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | This article highlights the various ways in which social media disinformation campaigns are propagated among US military personnel and veterans. By conducting and analyzing selected keywords, seed accounts, and known links to content, the researchers were able to construct large network visualizations and found that on Twitter there are significant and persistent interactions between current and former military personnel and a broad network of Russia-focused accounts, conspiracy theory focused accounts, and European right-wing accounts. These interactions are often mediated by pro-Trump users and accounts that identify with far-right political movements in the US. Similar interactions are also found on Facebook. | The authors’ definition of junk news includes various forms of propaganda and ideologically extreme, hyperpartisan, or conspiratorial political news and information. They note that much of this content is deliberately produced false reporting. The authors looked specifically at three “junk news” websites specializing in content on military affairs and national security issues for US military personnel and veterans: veteranstoday.com, veteransnewsnow.com, and southfront.org. | Twitter and Facebook | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | NA | ||||
80 | Barash | Bruce Etling and Rob Faris and John Palfrey and Urs Gasser and John Kelly and Karina Alexanyan and Vladimir Barash | Mapping Russian Twitter | 2012 | https://cyber.harvard.edu/publications/2012/mapping_russian_twitter | Report | Berkman Klein Center for Internet & Society at Harvard University | Characterize propaganda | Report propaganda | 0 | 1 | 0 | Drawing from a corpus of over 50 million Russian-language tweets collected between March 2010 and March 2011, we created a network map of 10,285 users comprising the ‘discussion core,’ and clustered them based on a combination of network features | he major topical groupings in Russian Twitter include: Political, Instrumental, CIS Regional, Technology, and Music. There are also several clusters centered on Russian regions, which is significant given the limited reach of the Internet in the regions outside of Moscow and St. Petersburg. Russian Twitter features a great deal of activity generated by marketing campaigns and search engine optimization (SEO) initiatives, including both automated and coordinated human actors. After our initial mapping resulted in a network dominated by these ‘instrumental’ actors, we constructed a filter to limit their presence in the network and discover relationships among a wider variety of ‘organic’ actors. Similar to the Russian blogosphere, the Twitter network includes a democratic opposition cluster associated with Gary Kasparov and the opposition Solidarity movement.1 In other respects the political clusters identified in Weblog and Twitter networks display interesting variation. Nationalists, who are very active in Russian blogs, do not appear to be organized in Russian Twitter (at least as of March 2011). Conversely, pro-Putin youth groups like the Young Guards and Nashi, and elected officials allied with them, have a distinct Twitter footprint. While other clusters within Twitter often mirrored those in Weblogs, such as one cluster focused on major bloggers and online personalities, there were some Twitter clusters that had no clear Weblog analog. Most notably, there are two clusters of Twitter users affiliated with local government administrations in Tver and Ivanovo, representing active outreach to citizens by local government actors. While the filtered version of the map successfully reduced the presence of SEO actors, it curiously eliminated a pro-government cluster as well. In the filtered map, whereas the number of political actors was greatly increased overall, a cluster in the original map that focused on President Medvedev’s economic modernization policy disappeared, along with related hashtags. One possibility is that, as we observed in the Russian blogosphere, some political initiatives have adopted the tactics and/or services of online marketers. | 50 million Russian-language tweets collected between March 2010 and March 2011 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | RUS | ||||
81 | Barash | Vidya Narayanan and Vlad Barash and Bence Kollanyi and Lisa-Maria Neudert and Philip N Howard | Polarization, Partisanship and Junk News Consumption over Social Media in the US | 2018 | http://comprop. oii.ox.ac.uk/wp-content/uploads/sites/93/2018/02/ Polarization-Partisanship-JunkNews.pdf | Working Paper | Computational Propaganda Research Project | Amplify and create disinformation | Polarize society | 0 | 1 | 0 | This article examines which groups on Facebook and Twitter are most likely to consume and share “junk news,” which the authors define as misleading, deceptive, or incorrect information purporting to be real news about politics, economics, or culture. This includes various forms of extremist, sensationalist, conspiratorial, masked commentary, and fake news. They ultimately find that on Twitter, Trump supporters followed by conservatives are most likely to circulate junk news, while on Facebook, extreme hard right pages (different from Republican pages) share the widest range of junk news sources. | There is little overlap in sources of news consumption between supporters of the Democratic Party and the Republican Party. They find that Democrats show high levels of engagement with mainstream media sources compared to Republicans and conservatives. • On Twitter, the “Trump Support Group” shares 95% of the junk news sites on the watch list, and accounted for 55% of junk news traffic in the sample. • On Facebook, the “Hard Conservative Group” shares 91% of the junk news sites on the watch list, and accounted for 58% of junk news traffic in the sample. | For this study, a seed of known propaganda websites across the political spectrum was used, drawing from a sample of 22,117,221 tweets collected during the US election, between November 1-11, 2016. (The full seed list is in the online supplement and available as a standalone spreadsheet.) | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | NA | ||||
82 | Barbera | Joshua A. Tucker and Yannis Theocharis and Margaret E. Roberts and Pablo Barberá | From Liberation to Turmoil: Social Media and Democracy | 2017 | https://muse.jhu.edu/article/671987/summary | Academic Journal | Journal of democracy | Amplify and create disinformation | Political polarization | 0 | 0 | 1 | Study cases | Five years social media have gone—in the popular imagination at least—from being a way for prodemocratic forces to fight autocrats to being a tool of outside actors who want to attack democracies. | Study cases | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | ||||
83 | Barbera | Marko Klašnja and Pablo Barberá and Nicholas Beauchamp and Jonathan Nagler and Joshua A. Tucker | Measuring Public Opinion with Social Media Data | 2018 | http://www.oxfordhandbooks.com/view/10.1093/oxfordhb/9780190213299.001.0001/oxfordhb-9780190213299-e-3 | Book | The Oxford Handbook of Polling and Survey Methods | Media consumption | Database using social media | 0 | 1 | 0 | Three challenges are discussed: identifying political opinion, representativeness of social media users, and aggregating from individual responses to public opinion. The chapter outlines some of the strategies for overcoming these challenges and proceeds by highlighting some of the novel uses for social media that have fewer direct analogs in traditional survey work. | it suggests new directions for a research agenda in using social media for public opinion work. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | NA | |||||
84 | Barbera | Joshua A. Tucker and AndrewGuess and PabloBarberá and Cristian Vaccari and AlexandraSiegel and Sergey Sanovich and DenisStukal and Brendan Nyhan | Social Media, Political Polarization, and Political Disinformation: A Review of the Scientific Literature | 2018 | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3144139 | Academic Journal | Review of scientific literature | Media consumption | Literature review | 0 | 1 | 1 | The review of the literature is provided in six separate sections, each of which can be read individually but that cumulatively are intended to provide an overview of what is known—and unknown—about the relationship between social media, political polarization, and disinformation | The report concludes by identifying key gaps in our understanding of these phenomena and the data that are needed to address them. | Literature review | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | ||||
85 | Barbera | Yannis Theocharis, Pablo Barberá, Zoltán Fazekas, and Sebastian Adrian Popa | The Dynamics of Political Incivility on Twitter | 2020 | https://journals.sagepub.com/doi/pdf/10.1177/2158244020919447 | Academic Journal | SAGE Open | Identify disinformation | Influence political discourse | 0 | 1 | 0 | We develop a conceptual framework for understanding the dynamics of incivility at three distinct levels: macro (temporal), meso (contextual), and micro (individual) | Using longitudinal data from the Twitter communication mentioning Members of Congress in the United States across a time span of over a year and relying on supervised machine learning methods and topic models, we offer new insights about the prevalence and dynamics of incivility toward legislators. We find that uncivil tweets represent consistently around 18% of all tweets mentioning legislators, but with spikes that correspond to controversial policy debates and political events. Although we find evidence of coordinated attacks, our analysis reveals that the use of uncivil language is common to a large number of users. | To initiate our data collection, we relied on the list of Twitter accounts of Members of Congress elected to the 115th Congress (2017–2018) available in the unitedstates GitHub account. | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | USA | ||||
86 | Barbera | Pablo Barbera, Andreu Casas, Jonathan Nagler, Patrick J. Egan, Richard Bonneau, John T. Jost, Joshua A. Tucker | Who Leads? Who Follows? Measuring Issue Attention and Agenda Setting by Legislators and the Mass Public Using Social Media Data | 2019 | https://www.cambridge.org/core/services/aop-cambridge-core/content/view/D855849CE288A241529E9EC2E4FBD3A8/S0003055419000352a.pdf/div-class-title-who-leads-who-follows-measuring-issue-attention-and-agenda-setting-by-legislators-and-the-mass-public-using-social-media-data-div.pdf | Academic Journal | American Political Science Review | Media consumption | Political participation | 0 | 1 | 0 | Are legislators responsive to the priorities of the public? Research demonstrates a strong correspondence between the issues about which the public cares and the issues addressed by politicians, but conclusive evidence about who leads whom in setting the political agenda has yet to be uncovered.We answer this question with fine-grained temporal analyses of Twitter messages by legislators and the public during the 113th US Congress. After employing an unsupervised method that classifies tweets sent by legislators and citizens into topics, we use vector autoregression models to explore whose priorities more strongly predict the relationship between citizens and politicians. | We find that legislators are more likely to follow, than to lead, discussion of public issues, results that hold even after controlling for the agenda-setting effects of the media. We also find, however, that legislators are more likely to be responsive to their supporters than to the general public. | To test our hypotheses, we use tweets sent by members of the 113th House and Senate of the US Congress (2013–14). | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | USA | ||||
87 | Barberá | Joshua A Tucker, Andrew Guess, Pablo Barberá, Cristian Vaccari, Alexandra Siegel, Sergey Sanovich, Denis Stukal, Brendan Nyhan | Social Media, Political Polarization, and Political Disinformation: A Review of the Scientific Literature | 2018 | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3144139 | Working Paper | SSRN | Amplify and create disinformation | Literature Review | 0 | 0 | 1 | The following report is intended to provide an overview of the current state of the literature on the relationship between social media; political polarization; and political “disinformation,” a term used to encompass a wide range of types of information about politics found online, including “fake news,” rumors, deliberately factually incorrect information, inadvertently factually incorrect information, politically slanted information, and “hyperpartisan” news. The review of the literature is provided in six separate sections, each of which can be read individually but that cumulatively are intended to provide an overview of what is known—and unknown—about the relationship between social media, political polarization, and disinformation. | The report concludes by identifying key gaps in our understanding of these phenomena and the data that are needed to address them. | Literature Review | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | NA | NA | ||||
88 | Barrera | Oscar Barrera, Sergei Guriev, Emeric Henry, Ekaterin Zhuravskayaa | Facts, alternative facts, and fact checking in times of post-truth politics | 2020 | https://www.sciencedirect.com/science/article/pii/S0047272719301859 | Academic Journal | Journal of Public Economics | Identify disinformation | Influence political discourse | 0 | 1 | 0 | In a randomized online experiment during the 2017 French presidential election campaign, we subjected subgroups of 2480 French voters to alternative facts by the extreme-right candidate, Marine Le Pen, and/or corresponding facts about the European refugee crisis from official sources. | We find that: (i) alternative facts are highly persuasive; (ii) fact checking improves factual knowledge of voters (iii) but it does not affect policy conclusions or support for the candidate; (iv) exposure to facts alone does not decrease support for the candidate, even though voters update their knowledge. We find evidence consistent with the view that at least part of the effect can be explained by raising salience of the immigration issue. | In March 2017, one month before the first round of the presidential election, we conducted an online survey of 2480 French voting-age individuals using the Qualtrics online platform, an analogue of the Amazon Mechanical Turk | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | USA | USA | ||||
89 | Baryshev | Mikhail Myagkov and Evgeniy V. Shchekotin and Vitaliy V. Kashpur and Vyacheslav L. Goiko and Alexey A. Baryshev | Activity of non-parliamentary opposition communities in social networks in the context of the Russian 2016 parliamentary election | 2018 | https://www.tandfonline.com/doi/full/10.1080/21599165.2018.1532411 | Academic Journal | Journal East European Politics | Characterize propaganda | Computational propaganda | 0 | 1 | 0 | This article aims to analyse the connection between the election to the Russian State Duma in September 2016 and the social network activity of members of the two non-parliamentary opposition groups: right-wing radicals and supporters of the opposition leader Alexei Navalny. For this purpose, the Right-Wing Online Activity Index and Navalny Supporters’Online Activity Index were calculated. Then, using correlation analysis, relationships between the indices and the results of voting for political parties in the election to the State Duma were determined. | As a result, the highest values of the Right-Wing and Navalny Supporters’Online Activity Indices were registered in Moscow and St. Petersburg. | The Right-Wing Online Activity Index and Navalny Supporters’ Online Activity Index were designed. The indices were calculated based on statistics obtained from the largest Russian social network VKontakte. | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | ||||
90 | Bassett | Cuihua Shen and Mona Kasra and Wenjing Pan and Grace A Bassett and Yining Malloch and James F O’Brien | Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online | 2018 | https://journals.sagepub.com/doi/full/10.1177/1461444818799526 | working paper | Sage | amplify and create disinformation | how individuals evaluate the authenticity of images that accompany online stories | 0 | 1 | 0 | a 6-batch large-scale online experiment using Amazon Mechanical Turk that probes how people evaluate image credibility across online platforms. In each batch, participants were randomly assigned to 1 of 28 news-source mockups featuring a forged image, and they evaluated the credibility of the images based on several features | participants’ Internet skills, photo-editing experience, and social media use were significant predictors of image credibility evaluation, while most social and heuristic cues of online credibility (e.g. source trustworthiness, bandwagon, intermediary trustworthiness) had no significant impact. Viewers’ attitude toward a depicted issue also positively influenced their credibility evaluation. | survey | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | NA | NA | ||||
91 | Bastos | Marco Bastos and Johan Farkas | IRA Propaganda on Twitter: Stoking Antagonism and Tweeting Local News | 2018 | http://muep.mau.se/bitstream/handle/2043/25894/FarkasBastos2018.pdf | Working Paper | International Conference on Social Media and Society | Characterize propaganda | Characteristics of IRA propaganda | 0 | 1 | 0 | Tweets were annotated using 19 control variables to investigate whether IRA operations on social media are consistent with classic propaganda models | The results show that the IRA operates a composite of user accounts tailored to perform specific tasks, with the lion’s share of their work focusing on US daily news activity and the diffusion of polarized news across different national contexts. | 4539 tweets posted by IRA-linked accounts | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | RUS | ||||
92 | Bastos | Marco T. Bastos and Dan Mercea | The Brexit Botnet and User-Generated Hyperpartisan News | 2017 | http://journals.sagepub.com/doi/10.1177/0894439317734157 | Academic Journal | Social Science Computer Review | Identify disinformation | Social bots play a key role in the spread of fake news | 0 | 1 | 0 | We compare active users to this set of political bots with respect to temporal tweeting behavior, the size and speed of retweet cascades, and the composition of their retweet cascades (user-to-bot vs. bot-to-bot) to evidence strategies for bot deployment | Our results move forward the analysis of political bots by showing that Twitterbots can be effective at rapidly generating small- to medium-sized cascades; that the retweeted content comprises user-generated hyperpartisan news, which is not strictly fake news, but whose shelf life is remarkably short; and, finally, that a botnet may be organized in specialized tiers or clusters dedicated to replicating either active users or content generated by other bots. | Network of Twitterbots comprising 13,493 accounts that tweeted the United Kingdom European Union membership referendum, only to disappear from Twitter shortly after the ballot | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | GBR | RUS | ||||
93 | Bear | Michael V. Bronstein and Gordon Pennycook and Adam Bear and David G. Rand and Tyrone D. Cannon | Belief in fake news is associated with delusionality, dogmatism, religious fundamentalism, and reduced analytic thinking. | 2018 | https://www.sciencedirect.com/science/article/pii/S2211368118301050 | Academic Journal | Journal of Applied Research in Memory and Cognition | Media consumption | Fake news and who consume them | 0 | 1 | 0 | Delusion-prone individuals may be more likely to accept even delusion-irrelevant implausible ideas because of their tendency to engage in less analytic and less actively open-minded thinking. Consistent with this suggestion, two online studies with over 900 participants demonstrated that although delusion-prone individuals were no more likely to believe true news headlines, they displayed an increased belief in “fake news” headlines, which often feature implausible content. Mediation analyses suggest that analytic cognitive style may partially explain these individuals’ increased willingness to believe fake news. | Exploratory analyses showed that dogmatic individuals and religious fundamentalists were also more likely to believe false (but not true) news, and that these relationships may be fully explained by analytic cognitive style. Our findings suggest that existing interventions that increase analytic and actively open-minded thinking might be leveraged to help reduce belief in fake news. | Participants were recruited via Amazon’s Mechanical Turk (MTurk) in two waves (Study 1: n = 502, Study 2: n = 446; Demographics: SI Section S1). Only participants who were over 18 and who lived in the United States were recruited. | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | USA | NA | ||||
94 | Bega | Todd C. Helmus and Elizabeth Bodine-Baron and Andrew Radin and Madeline Magnuson and Joshua Mendelsohn and William Marcellino and Andriy Bega and Zev Winkelman | Russian Social Media Influence Understanding Russian Propaganda in Eastern Europe | 2018 | https://www.rand.org/pubs/research_reports/RR2237.html | Report | Rand corporation | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | Examined Russian-language content on social media and the broader propaganda threat posed to the region of former Soviet states | In the Baltics, Ukraine, and other nearby states, the Kremlin aims to drive wedges between ethnic Russian or Russian-speaking populations and their host governments, NATO, and the European Union. Farther abroad, the Kremlin attempts to achieve policy paralysis by sowing confusion, stoking fears, and eroding trust in Western and democratic institutions. | Web pages, twitter accounts | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | NA | RUS | ||||
95 | Bellamy | Carl Ehrett, Darren L. Linvill, Hudson Smith, Patrick L. Warren, Leya Bellamy, Marianna Moawad, Olivia Moran, and Monica Moody | Inauthentic Newsfeeds and Agenda Setting in a Coordinated Inauthentic Information Operation | 2021 | https://journals.sagepub.com/doi/abs/10.1177/08944393211019951?casa_token=67olAiVnnwUAAAAA%3AdOpnXebBm8x14XTIKTwwLKM6Yo-WsXfj8FTRdGWDE6jcHec6shqkPmudvuQfR0nsSF7azKa5VeS9zA&journalCode=ssce | Academic Journal | Social Science Computer Review | Characterize propaganda | Influence of a foreign country in the US | 0 | 1 | 0 | The 2015–2017 Russian Internet Research Agency (IRA)’s coordinated information operation is one of the earliest and most studied of the social media age. A set of 38 city-specific inauthentic “newsfeeds” made up a large, underanalyzed part of its English-language output. We label 1,000 tweets from the IRA newsfeeds and a matched set of real news sources from those same cities with up to five labels indicating the tweet represents a world in unrest and, if so, of what sort. We train a natural language classifier to extend these labels to 268 k IRA tweets and 1.13 million control tweets | Compared to the controls, tweets from the IRA were 34% more likely to represent unrest, especially crime and identity danger, and this difference jumped to about twice as likely in the months immediately before the election. Agenda setting by media is well-known and well-studied, but this weaponization by a coordinated information operation is novel. | First, we collected the output of the 38 locally oriented IRA newsfeed accounts from Twitter’s January 2019 update to their October 2018 release of the output they linked to the IRA (Roth, 2019), for the 13 months running from December 2015 to December 2016. We supplement these with 60,975 additional tweets produced from these same accounts during this period and downloaded from Social Studio but not included in the Twitter release | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | RUS | doi-org.ez.urosario.edu.co/10.1177/08944393211019951 | |||
96 | Benevenuto | Julio C. S. Reis, Philipe de Freitas Melo, Kiran Garimella, Fabrício Benevenuto | Detecting Misinformation on WhatsApp without Breaking Encryption | 2020 | https://arxiv.org/abs/2006.02471 | Working Paper | Cornell University | Identify disinformation | Influence political discourse | 0 | 1 | 0 | Due to the private encrypted nature of the messages it is hard to track the dissemination of misinformation at scale. In this work, we propose an approach for WhatsApp to counter misinformation that does not rely on content moderation. The idea is based on on-device checking, where WhatsApp can detect when a user shares multimedia content which have been previously labeled as misinformation by fact-checkers, without violating the privacy of the users | We evaluate the potential of this strategy for combating misinformation using data collected from both fact-checking agencies and WhatsApp during recent elections in Brazil and India. Our results show that our approach has the potential to detect a considerable amount of images containing misinformation, reducing 40.7% and 82.2% of their shares in Brazil and India, respectively. | To gather the data explored in this work we use available tools (Garimella and Tyson 2018) to get access to messages posted on public WhatsApp groups. selected over 400 and 4,200 groups from Brazil and India, respectively, dedicated to political discussions. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | BRA, IND | BRA, IND | ||||
97 | Benito | Javier Borondo and Alfredo J. Morales-Guzman and Juan Carlos Losada González and Rosa M. Benito | Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish Presidential election as a case study | 2012 | https://aip.scitation.org/doi/full/10.1063/1.4729139 | working Paper | AIP | Amplify and create disinformation | Electoral campaigns | 0 | 1 | 0 | we use temporal series and complex network analysis to unveil the users’ behavioral patterns during the 2011 Spanish presidential electoral campaign in Twitter. we characterize the users and politicians’ interactions and propose a model to simulate their behavior. introduce a new measure to study political sentiment in Twitter, which we call the relative support. We have also characterized user behavior by analyzing the structural and dynamical patterns of the complex networks emergent from the mention and retweet networks | user activity is correlated to the election’s outcome and we propose a new parameter to measure the relative support (RS) of candidate.a profound lack of debate among the users and politicians, who mainly participated to campaign and whose attention was drawn by a small fraction of influential users. | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | Spain | NA | 10.1063/1.4729139 | ||||
98 | Benito | S. Martin-Gutierrez and J. C. Losada and R. M. Benito | Recurrent Patterns of User Behavior in Different Electoral Campaigns: A Twitter Analysis of the Spanish General Elections of 2015 and 2016 | 2018 | https://www.hindawi.com/journals/complexity/2018/2413481/abs/ | Academic Journal | Complexity | Amplify and create disinformation | Manipulate opinions and democratic elections | 0 | 1 | 0 | We have retrieved and analyzed several millions of Twitter messages corresponding to the Spanish general elections held on the 20th of December 2015 and repeated on the 26th of June 2016. Te availability of data from two electoral campaigns that are very close in time allows us to compare collective behaviors of two analogous social systems with a similar context. | By computing and analyzing the time series of daily activity, we have found a signifcant linear correlation between both elections. Additionally, we have revealed that the daily number of tweets, retweets, and mentions follow a power law with respect to the number of unique users that take part in the conversation. Furthermore, we have verifed that the topologies of the networks of mentions and retweets do not change from one election to the other, indicating that their underlying dynamics are robust in the face of a change in social context. Hence, in the light of our results, there are several recurrent collective behavioral patterns that exhibit similar and consistent properties in diferent electoral campaigns. | Twitter messages retrieved with the Twitter Streaming API. We have chosen the following neutral keywords to flter the messages: (i) Keywords for the 2015 election: 20D, 20D2015, #EleccionesGenerales2015 (ii) Keywords for the 2016 election: 26J, 26J2016, #EleccionesGenerales2016, #Elecciones26J We have downloaded tweets during a period of more than two months before and afer each election. However, the core of our analysis has been focused on the 15 days of the ofcial electoral campaign, the refection day (day before the election), the election day, and the day afer. During that period of 18 days we have retrieved 1793145 tweets for the 2015 election and 1755438 for the 2016 election | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | ESP | NA | ||||
99 | Benkler | Robert Faris and Hal Roberts and Bruce Etling and Nikki Bourassa and Ethan Zuckerman and Yochai Benkler | Partisanship, Propaganda, and Disinformation: Online Media and the 2016 U.S. Presidential Election | 2017 | https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3019414 | Working Paper | SSRN | Characterize propaganda | Report propaganda | 0 | 1 | 0 | In this study, we analyze both mainstream and social media coverage of the 2016 United States presidential election. We document that the majority of mainstream media coverage was negative for both candidates, but largely followed Donald Trump’s agenda: when reporting on Hillary Clinton, coverage primarily focused on the various scandals related to the Clinton Foundation and emails. When focused on Trump, major substantive issues, primarily immigration, were prominent. Indeed, immigration emerged as a central issue in the campaign and served as a defining issue for the Trump campaign. | We find that the structure and composition of media on the right and left are quite different. The leading media on the right and left are rooted in different traditions and journalistic practices. On the conservative side, more attention was paid to pro-Trump, highly partisan media outlets. On the liberal side, by contrast, the center of gravity was made up largely of long-standing media organizations steeped in the traditions and practices of objective journalism. Our data supports lines of research on polarization in American politics that focus on the asymmetric patterns between the left and the right, rather than studies that see polarization as a general historical phenomenon, driven by technology or other mechanisms that apply across the partisan divide. | Media stories published on the web and shared on Twitter. | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | NA | ||||
100 | Bentley | Damian J. Ruck and Natalie Manaeva Rice and Joshua Borycz and R. Alexander Bentley | Internet Research Agency Twitter activity predicted 2016 U.S. election polls | 2019 | https://firstmonday.org/article/view/10107/8049 | Working Paper | first Monday | Amplify and create disinformation | Election oopinion polling vs number of likes by IRA | 0 | 1 | 0 | ypothesized that this affected public opinion during the 2016 U.S. presidential election. Here we test that hypothesis using vector autoregression (VAR) comparing time series of election opinion polling during 2016 versus numbers of re-tweets or ‘likes’ of IRA tweets. | changes in opinion poll numbers for one of the candidates were consistently preceded by corresponding changes in IRA re-tweet volume, at an optimum interval of one week before. In contrast, the opinion poll numbers did not correlate with future re-tweets or ‘likes’ of the IRA tweets. We find that the release of these tweets parallel significant political events of 2016 and that approximately every 25,000 additional IRA re-tweets predicted a one percent increase in election opinion polls for one candidate. | tweets | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | USA | RUS |