Crisis Informatics and Coordination Research
Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)
Wright State University, USA
- Prof. Amit Sheth (PI), amit@ knoesis.org
- Hemant Purohit (Student coordinator), hemant@ knoesis.org
- Prof. Valerie Shalin, Prof. John Flach (Psychology, Wright State U)
- Prof. Srinivasan Parthasarathy (Network Science, Ohio State U)
- Dr. Patrick Meier, Dr. Carlos Castillo (Crisis Computing, Qatar Computing Research Institute)
- Dr. Fernando Diaz (Crisis Computing, Microsoft Research)
- Oshani Seneviratne (Ontology for Crisis-Info Mgmt, CSAIL, MIT)
Hemant Purohit, Shreyansh Bhatt, Drew Hampton (WSU); Yiye Ruan, Dave Fuhry (OSU)
- AAAI conference ICWSM 2013, details/slides and tutorial program
- SIAM conference SDM 2014, details/slides and tutorial program
- Interdisciplinary project of Social and Computer Science teams: NSF SoCS IIS-1111182: Social Media Enhanced Organizational Sensemaking in Emergency Response
- Background on this project
- Insight page that shows how we used our Twitris system in the #Oklahoma Tornado disaster response. Also check some of the snapshots from video
- H. Purohit, A. Hampton, V. Shalin, A. Sheth, J. Flach, S. Bhatt. What Kind of #Communication is Twitter? Mining #Psycholinguistic Cues for Emergency Coordination, Computers in Human Behavior (CHB) journal, Vol. 29, Issue 6, Nov. 2013, P 2438–2447. [Cited by CDC’s Health Communication Science Digest’s October issue]
- H. Purohit, C. Castillo, F. Diaz, A. Sheth and P. Meier. Emergency-Relief Coordination on Social Media: Automatically Matching Resource Requests and Offers. First Monday, Vol. 19, Issue 1, Jan 2014. [Highlighted on the front page of First Monday journal for January issue]
- H. Purohit, A. Hampton, S. Bhatt, V. Shalin, A. Sheth, J. Flach. Identifying Seekers and Suppliers in Social Media Communities to Support Crisis Coordination. Journal of CSCW, Springer, 2014. DOI: 10.1007/s10606-014-9209-y
- H. Purohit, S. Bhatt, A. Hampton, V. Shalin, A. Sheth, J. Flach. With Whom to Coordinate, Why and How in Ad-hoc Social Media Communities during Crisis Response. ISCRAM, 2014.
- S. Bhatt, H. Purohit, A. Hampton, V. Shalin, A. Sheth, J. Flach. Assisting coordination during crisis: a domain ontology based approach to infer resource needs from tweets. Proceedings of the 2014 ACM conference on Web science, 297-298
- A. Sheth, A. Jadhav, P. Kapanipathi, C. Lu, H. Purohit, G. A. Smith, W. Wang, Twitris- a System for Collective Social Intelligence. Encyclopedia of Social Network Analysis and Mining (ESNAM), Springer, 2014. (To appear)
- H. Purohit and A. Sheth. Twitris v3: From Citizen Sensing to Analysis, Coordination and Action. The 7th Int’l AAAI Conference on Weblogs and Social Media, ICWSM 2013, Demo track.
- H. Purohit, A. Hampton, V. Shalin, A. Sheth, J. Flach. Framework to Analyze Coordination in Crisis Response. Collaboration and Crisis Informatics, Workshop in conjunction with CSCW-2012. (Position paper)
- H. Purohit. Crisis Response Coordination in Online Communities. Doctoral Consortium at NSF SOCS PI Meet, 2013.
- H. Purohit, A. Hampton, V. Shalin, A. Sheth, J. Flach. What kind of #communication is Twitter? A psycholinguistic perspective on communication in Twitter for the purpose of emergency coordination. NSF SoCS Symposium, 2012.
- A. Smith, A. Sheth, A. Jadhav, H. Purohit, L. Chen, M. Cooney, P. Kapanipathi, P. Anantharam, P. Koneru and W. Wang. Twitris+: Social Media Analytics Platform for Effective Coordination. NSF SoCS Symposium, 2012.
- A. Jadhav, H. Purohit, P. Kapanipathi, P. Ananthram, A. Ranabahu, V. Nguyen, P. Mendes, A. G. Smith, M. Cooney, A. Sheth Twitris 2.0: Semantically Empowered System for Understanding Perceptions From Social Data, Semantic Web Application Challenge, ISWC 2010.
- M. Nagarajan, K. Gomadam, A. Sheth, A. Ranabahu, R. Mutharaju and A. Jadhav, Spatio-Temporal-Thematic Analysis of Citizen-Sensor Data - Challenges and Experiences, Tenth International Conference on Web Information Systems Engineering, October 5-7, 2009, 539 - 553.
- A. Sheth, Citizen Sensing,Social Signals, and Enriching Human Experience, IEEE Internet Computing, July/August 2009, pp. 80-85.
- AAAI conference ICWSM-2013 tutorial: by Hemant Purohit & Amit Sheth with Patrick Meier & Carlos Castillo at QCRI: Crisis Mapping, Citizen Sensing and Social Media Analytics: Leveraging Citizen Roles for Crisis Response, July 2013 (slides and info)
- SIAM conference SDM-2014 tutorial: by Hemant Purohit with Carlos Castillo at QCRI and Fernando Diaz at MSR: Leveraging Social Media and Web of Data for assisting Crisis Response Coordination, Apr 2014 (details)
- Ignite Talk by Hemant Purohit at ICCM-2013 (CrisisMappers.net conference) at UN Nairobi, on ‘How to Leverage Social Media Communities for Crisis Response Coordination’, Nov 2013
- Guest lectures “Crisis Informatics and Coordination: Leveraging Citizen Roles via Social Media for Disaster Response”, Course I400/I590: Informatics in Disasters and Emergency Response, Indiana University, Fall 2013
- Positioning a Coordination analysis Framework #CSCW 2012: (presentation 1, same as 2)
- Citizen Sensing-Opportunities and Challenges in Mining Social Signals and Perceptions #Microsoft-faculty-summit, 2011 (invited talk)
- Semantic Integration of Citizen Sensor Data and Multilevel Sensing: A comprehensive path towards event monitoring and situational awareness #SEMTECH 2009: (keynote)
- Twitris: A 360 degree social media analytics platform to assist decision making by providing multi-faceted analyses of social data: Spatio-Temporal-Thematic, People-Content-Network, Sentiment-Emotion-Subjectivity etc.
- SoCS Ontology for Crisis Coordination (SOCC): We extend the concepts of domain knowledge-driven models, MOAC- Management Of A Crisis ontology (Limbu 2012), and UNOCHA's HXL- Humanitarian Exchange Language (Keßler et al. 2013) ontology, with required but missing concepts for organizing data during crisis response coordination for seeker and supplier behavior, and indicators of resource needs using a lexicon. For example, the 'shelter' class contains words 'emergency center,' 'tent,' and 'shelter,' along with lexical alternatives. For the present demonstration, we focus on three resource categories: food, shelter and medical needs. Thus, we endeavor to exploit a minimum, but always expandable subset that provides the maximum coverage while controlling false alarms. For creating lexicons of indicator words for concepts, we relied on various documents collected via interactions with domain experts (Flach et al. 2013), our Community Emergency Response Team (CERT) training, Rural Domestic Preparedness Consortium training, and publically available references (Homeland Security 2010; FEMA 2012; OCHA,Verity 2011). Using a first aid handbook (Swienton and Subbarao 2012), we created an extensive 'medical' subset of emergency indicators, where we identified words which pertained specifically to first aid or injuries and included those words along with variations in tense (i.e., breath, breathing, breathes) and common abbreviations (i.e. mouth to mouth, mouth 2 mouth, CPR). A local expert with FEMA experience augmented the model with additional indicators and provided anecdotal context. The current model with food, medical, and shelter resource indicators contain 43 concepts and 45 relationships. We created this domain model in the OWL language using the Protégé ontology editor (Protégé 2013). Each type of disaster is listed as an entity type with indicators for that disaster listed as individuals under a corresponding indicator entity. Therefore a relationship is declared stating that a particular disaster concept, say Flood, relates by property 'has_a_positive_indicator', with 'Flood_i' indicator entity, that includes all words under that heading. Each disaster has a declared negative relationship with the negative indicator list (e.g., 'erotic' under sexual words indicators) under the entity name Negative_Indicator_i. Finally resources are declared as individuals under the appropriate entity in the same way, but relationships are not explicitly stated with any disaster in order to provide flexibility. [Read more: Purohit et al., JCSCW 2014]
Relevant Media/Blog stories on our initiatives:
- North India Floods (crisis-map): Using crisis mapping to aid Uttarakhand, The Hindu, Jun 27, 2013, Creating awareness for tech-assisted response: Are we missing out on tech-aided disaster management in Uttarakhand?, The Hindu, Jul 17, 2013
- Phailin Cyclone (crisis-map): Times Of India, DNA, Business Standard, EfyTimes, Kochi Reporter, Gadget Garrio, GeoSpatial World, Global Resilience Systems, Oct 2013
IME (Identify-Match-Engage) Computing Framework: (Purohit, PhD dissertation proposal, 2014)
(Purohit, PhD dissertation proposal, 2014)
Fig 1. Positioning for coordination analysis framework: (Purohit et. al 2012)
Fig 2. Implemented Coordination analysis framework: (Purohit 2013, Purohit et. al 2014b)
Fig 3. Example of Coordination during #Oklahoma-tornado response (details) (Science: Purohit et al. 2014a : Highlighted on front page of First Monday journal, for the January Issue
Fig 4. Twitris engagement interface to assist coordination: (Purohit 2013, Purohit et. al 2013)
Fig 5. Topical influencers to engage with based on specific resource needs and their occupational types (Purohit et al. 2014c)
Fig 6. Other features of Twitris research tool to monitor social media for assisting disaster situation monitoring (examples during Oklahoma tornado 2013): (Purohit & Sheth, 2013, Sheth et al., 2014)