BA Seminar

Big Data and Social Science

(Fall 2018)

Description:        The seminar introduces students to the basic concepts underlying social data science in the age of big data.

Time and place:        Friday 9:15–12:00, 13:00–17:00 (Dates: 12.10., 26.10, 9.11, 23.11), SOWI PC-Pool, B003, vonRoll, Fabrikstrasse 8

Instructor:        Paul C. Bauer; University of Mannheim, Mannheim Centre of European Social Research

Email:         paul.bauer@mzes.uni-mannheim.de

Office hours:        Possible over skype; Write me to make an appointment

Course outline

In the wake of the digital revolution, societies store an ever increasing amount of data on humans and their behavior. In parallel, increases in computational power allow for more meaningful interpretations of such data. This enables social scientists to approach old questions with new methods, but also to study entirely new questions.

The seminar introduces students to different aspects of this “big data revolution”. It comprises theoretical sessions in which discuss the implications of this revolution such as its societal up and downsides, the data that is collected (from  social media, communications platforms, Internet of Things devices, sensors/wearables, and mobile phones, digitized old data records) and the analysis of such data. And it comprises lab sessions in which we learn - hands on - how to tap various of the those new data sources that are available to us and ways of analyzing them. In addition, students will work on their own project relying on such new forms of data.

Participation requirements

In order to obtain credits & a grade participants are required to

1. attend regularly (at least 80% of the sessions) and participate actively. A maximum of two sessions can be missed without excuse. Absence in further sessions can only be excused in case of illness (i. e. with a medical certificate).
2. finish and hand in any exercises (e.g. datacamp) given during the seminar.
3. hand in a project plan and present it if there is time (Deadline: 23.11.2018).
4. write a final data science report on your project (Deadline: 31.1.2018).

Prerequisites

• Basic knowledge of R, either because of previous work in R or through self-study, e.g. on DataCamp.
• Some introductory statistics course covering regression analysis, frequentist statistics, frequentist hypothesis testing etc.

Evaluation criteria

You need to submit exercises and a project plan to pass the course. The final report/project will constitute your entire grade for the course. You may write the project individually or in groups of max. 2 people. All members of a group will get the same grade. 

• Data science report

• Around ~12-15 pages text without graphs/tables; Accompanied by a replication zip folder with code/data to reproduce the analysis in R/STATA/Python;
• Written in the style of a scientific paper.
• Describes the data source(s) you are using.
• Starts from a research question.
• A descriptive analysis is sufficient for your data science report.

Material & Readings

The seminar is based on the script “Big data and social science” (see below). The script will be continuously updated, contains all the slides and some further illustrations. There are various additional readings. These comprise fundamental texts but also studies which we will examine/replicate during the lab sessions in the seminar. I will distribute a link via email through which you have access to the literature. Please contact me in case you didn’t receive the link.

Essential readings:

• Script: https://bookdown.org/paul/big-data6/
• Mayer-Schönberger, Viktor, and Kenneth Cukier. 2012. “Big Data: A Revolution That Transforms How We Work, Live, and Think.” Houghton Mifflin Harcourt Boston.
• Helbing, D., Frey, B. S., Gigerenzer, G., Hafen, E., Hagner, M., Hofstetter, Y., ... & Zwitter, A. (2017). Will democracy survive big data and artificial intelligence. Scientific American, 25.
• Salganik, Matthew J. Bit by bit: social research in the digital age. Princeton University Press, 2017. (https://www.bitbybitbook.com/en/1st-ed/preface/)
• Lazer, D. & and Radford, J. (2017). Data ex Machina: Introduction to Big Data. Annual Review of Sociology.
• Tucker, J. A., Theocharis, Y., Roberts, M. E., & Barberá, P. (2017). From Liberation to Turmoil: Social Media And Democracy. Journal of Democracy, 28(4), 46-59.

Additional readings:

• Dressel, Julia, and Hany Farid. "The accuracy, fairness, and limits of predicting recidivism." Science advances 4.1 (2018): eaao5580.
• Barberá, Pablo. "Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data." Political Analysis 23.1 (2015): 76-91.
• Edelman, Benjamin, Michael Luca, and Dan Svirsky. 2017. “Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment.” American Economic Journal. Applied Economics 9 (2): 1–22.
• Lazer, David, Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014. “Big Data. The Parable of Google Flu: Traps in Big Data Analysis.” Science 343 (6176): 1203–5.
• Swan, Melanie. 2013. “The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery.” Big Data 1 (2): 85–99.
• Göbel, Sascha, and Simon Munzert. 2018. “Political Advertising on the Wikipedia Marketplace of Information.” Social Science Computer Review 36 (2): 157–75.
• Przepiorka, Wojtek, Lukas Norbutas, and Rense Corten. 2017. “Order without Law: Reputation Promotes Cooperation in a Cryptomarket for Illegal Drugs.” European Sociological Review 33 (6): 752–64.
• Narayanan, Arvind, and Vitaly Shmatikov. "Robust de-anonymization of large sparse datasets." Security and Privacy, 2008. SP 2008. IEEE Symposium on. IEEE, 2008.
• Zimmer, Michael. 2010. “‘But the Data Is Already Public’: On the Ethics of Research in Facebook.” Ethics and Information Technology 12 (4): 313–25.

Programm

The program is a mix theoretical and applied sessions. For a detailed program I refer you to the script (see Essential readings above).

References

Botsman, Rachel. 2017. “Big Data Meets Big Brother as China Moves to Rate Its Citizens.” WIRED UK. 2017. https://www.wired.co.uk/article/chinese-government-social-credit-score-privacy-invasion.

Curran, Dylan. 2018. “Are You Ready? This Is All the Data Facebook and Google Have on You.” The Guardian, March 30, 2018. http://www.theguardian.com/commentisfree/2018/mar/28/all-the-data-facebook-google-has-on-you-privacy.

Hvistendahl, Mara, Klint Finley, Zachary Karabell, Brett Solomon, Nicholas Thompson, Jessi Hempel, and Louise Matsakis. 2017. “Inside China’s Vast New Experiment in Social Ranking.” Wired, December 14, 2017. https://www.wired.com/story/age-of-social-credit/.

Isaac, Mike, and Sheera Frenkel. 2018. “Facebook Security Breach Exposes Accounts of 50 Million Users.” The New York Times, September 28, 2018. https://www.nytimes.com/2018/09/28/technology/facebook-hack-data-breach.html.

Jeffries, Daniel. 2018. “Big Brother Meets Black Mirror in the Middle Kingdom.” Hacker Noon. Hacker Noon. April 16, 2018. https://hackernoon.com/big-brother-meets-black-mirror-in-the-middle-kingdom-3febe4574467.

King, Gary. 2016. Big Data Is Not about the Data! Youtube. https://www.youtube.com/watch?v=mrb6tdVsVN0.

Marr, Bernard. 2015a. “How Big Data And The Internet Of Things Improve Public Transport In London.” Forbes Magazine, May 27, 2015. https://www.forbes.com/sites/bernardmarr/2015/05/27/how-big-data-and-the-internet-of-things-improve-public-transport-in-london/.

———. 2015b. “How Big Data Drives Success At Rolls-Royce.” Forbes Magazine, June 1, 2015. https://www.forbes.com/sites/bernardmarr/2015/06/01/how-big-data-drives-success-at-rolls-royce/.

———. 2015c. “Big Data At Dickey’s Barbecue Pit: How Analytics Drives Restaurant Performance.” Forbes Magazine, June 2, 2015. https://www.forbes.com/sites/bernardmarr/2015/06/02/big-data-at-dickeys-barbecue-pit-how-analytics-drives-restaurant-performance/.

Mayer-Schönberger, Viktor, and Kenneth Cukier. 2012. “Big Data: A Revolution That Transforms How We Work, Live, and Think.” Houghton Mifflin Harcourt Boston. https://scholar.google.ca/scholar?cluster=1384193364148379595,10996646448578669407,6407941109629666306,5630909684518876472,9299258551774110635,13234734498874729479,8836497654985406323,1528070241175170076,12864002250625774493,3298553394385623490,18410964651783895413,18090370349041515462,15706376482971344858,9297225840074412620,5526740653148018708,13380006091876103222,1128172653893271764,16409112337027432303,6823936900167040500,4203096401824009080,16908170737973033459,1299927491608396880,15358568506223857856,3774000160666587692,7892424541494232655,12286528769179400889,14627168448137962291,1742345652219765736,4368609118142112696&hl=en&as_sdt=0,5&sciodt=0,5.

McArdle, Megan. 2018. “People Are Getting Fired for Old Bad Tweets. Here’s How to Fix It.” The Washington Post, July 24, 2018. https://www.washingtonpost.com/opinions/we-need-a-statute-of-limitations-on-bad-tweets/2018/07/24/a84e335c-8f7d-11e8-b769-e3fff17f0689_story.html.

Tay, Liz. 2015. “Telstra Deploys Hadoop to Pre-Empt Network Issues.” iTnews. 2015. http://www.itnews.com.au/news/telstra-deploys-hadoop-to-pre-empt-network-issues-404526.

The Economist. 2018. “China Has Turned Xinjiang into a Police State like No Other.” The Economist. The Economist. 2018. https://www.economist.com/briefing/2018/05/31/china-has-turned-xinjiang-into-a-police-state-like-no-other.

Wikipedia contributors. 2018a. “Digital Revolution.” Wikipedia, The Free Encyclopedia. September 24, 2018. https://en.wikipedia.org/w/index.php?title=Digital_Revolution&oldid=860980130.

———. 2018b. “Nosedive (Black Mirror).” Wikipedia, The Free Encyclopedia. September 30, 2018. https://en.wikipedia.org/w/index.php?title=Nosedive_(Black_Mirror)&oldid=861799490.