Online social networks and always-connected mobile devices have created an immense opportunity that empowers citizens and organizations to communicate and coordinate effectively in the wake of critical events. Specifically, there have been many isolated examples of using Twitter to provide timely and situational information about emergencies to relief organizations, and to conduct ad-hoc coordination. However, there are few attempts that try to understand the full ramifications of using social networks in a more concerted manner for effective organizational sensemaking in such contexts. This multi-disciplinary project, spanning computational and social sciences, seeks to fill this gap.
This project seeks to leverage Twitter posts (tweets) as the primary source of citizen inputs and couple relevant content and network information along with microworld simulations involving human role players to measure effectiveness of various organized sensemaking strategies. To arrive at meaningful summaries of citizen input, tweet content is analyzed using a semantic content analysis by combining natural language techniques that are suitably fused with existing knowledge bases (GeoNames, Wikipedia). Content analysis is further enhanced by innovatively combining it with the dynamic analysis of the twitter network to realize concise and trustworthy information nuggets of potential interest to organizations and citizens. The resulting summaries will be fed to a suitably designed microworld simulation involving human actors to derive realistic settings for modeling disaster situations and typical organizational structures.
This project is expected to have a significant impact in the specific context of disaster and emergency response. However, elements of this research are expected to have much wider utility, for example in the domains of e-commerce, and social reform. From a computational perspective, this project introduces the novel paradigm of people-content-network analysis whose application is not just limited to organized sensemaking. For social scientists, it provides a platform that can be used to assess relative efficacy of various organizational structures using microworld simulations and is expected to provide new insights into the types of social network structures (mix of symmetric and asymmetric) that might be better suitable to propagate information in emergent situations. From an educational standpoint, the majority of funds will be used to train the next generation of interdisciplinary researchers drawn from the computational and social sciences. Participation of underrepresented groups, especially women, will be encouraged, and is anticipated. Datasets and software developed as part of this project will be made available to the broader research community here.
Keywords: Social Networking, Emergency Response, Content Analysis, Network Analysis, Organizational Sensemaking, Collaborative Decision Making.
Collaborative team of Wright State University (WSU) and Ohio State University (OSU):
WSU PI/PM: Amit Sheth
WSU Co-PIs: Valerie Shalin, John Flach (Department of Psychology, Human Factors/Industrial Organization Graduate Program)
OSU PI: Srinivasan Parthasarthy
This collaborative research is funded by the National Science Foundation under award IIS-1111182 to Wright State University (PI: Amit Sheth) and award IIS-1111118 to Ohio State University (PI: Srinivasan Parthasarthy), 09/01/2011 - 08/31/2014.
- About capability of Twitris system to analyze evolving events:
- Twitris Social Media Analysis Tackles Occupy Wall Street, 2012 Elections, Semanticweb.com, Feb. 10, 2012
- Twitris-Semantic Social Web Application, Digital Flicks, Feb 10, 2012
- Web App Analyzes Tweets in Real Time for a Record of Historic Events, Mashable, Feb. 17, 2012
- Twitris: Social Media Analysis with Semantic Web Technology, New Tech Post, Apr 9, 2012
- Picking the President: Twindex, Twitris Track Social Media Electorate, Semanticweb.com, Aug. 3, 2012
- Election 2012: The Semantic Recap, Semanticweb.com, Nov. 8, 2012
- Analyzing 2 Million Disaster Tweets from Oklahoma Tornado, iRevolution, May 29, 2013
- 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.
Talks and tutorials:
- A. Sheth, C. Castillo, P. Meier, H. Purohit. Crisis Mapping, Citizen Sensing and Social Media Analytics: Leveraging Citizen Roles for Crisis Response. ICWSM-13 tutorial, July 2013. (details)
- Dynamics of coordination behavior during emergency situations
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, 2013. (Accepted, in publication process)
- H. Purohit, A. Hampton, S. Bhatt, V. Shalin, A. Sheth, J. Flach. An Information Filtering and Management Model for Twitter Traffic to Assist Crises Response Coordination. Technical Report, Kno.e.sis Center, 2013. (Under Review)
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.
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.
- Purohit et al. Automatically matching resource needs and offers for coordination of emergency relief resources. (Under Review; available upon request)
- Understanding community engagement and sustainability on Twitter
H. Purohit, Y. Ruan, D. Fuhry, S. Parthasarthy, A. Sheth. On the Role of Social Identity and Cohesion in Characterizing Online Social Communities. Technical Report, 2012. (Under Review)
Y. Ruan, H. Purohit, D. Fuhry, S. Parthasarthy, A. Sheth. Prediction of Topic Volume on Twitter. 4th Int'l ACM Conference on Web Science (WebSci), 2012.
Y. Ruan. On the Interplay of Social Network Connection, User and Content. Doctoral Symposium at NSF SoCS Symposium, 2012.
H. Purohit, Y. Ruan, A. Joshi, S. Parthasarathy, A. Sheth. Understanding User-Community Engagement by Multi-faceted Features: A Case Study on Twitter. 1st Int’l Workshop on Social Media Engagement (SoME), 2011.
- Influence analysis in evolving communities on social media
H. Purohit, J. Ajmera, S. Joshi, A. Verma, A. Sheth. Finding Influential authors in Brand-page Communities. In Proceedings of the 6th Int'l AAAI Conference on Weblogs and Social Media (ICWSM), 2012.
- Extracting diverse sentiment expression with target dependent polarity and emotion identification
L. Chen, W. Wang, M. Nagarajan, S. Wang and A. Sheth. Extracting Diverse Sentiment Expressions with Target-dependent Polarity from Twitter. In Proceedings of the 6th International AAAI Conference on Weblogs and Social Media (ICWSM), 2012.
L. Chen. Extracting Diverse Sentiment Expressions With Target-dependent Polarity from Twitter. Doctoral Symposium at NSF SoCS Symposium, 2012.
Wenbo Wang, Lu Chen, Krishnaprasad Thirunarayan and Amit P. Sheth. Harnessing Twitter ‘Big Data’ for Automatic Emotion Identification . In Proceedings of International Conference on Social Computing (SocialCom), 2012.
Wenbo Wang, Lu Chen, Ming Tan, Shaojun Wang, Amit P. Sheth. Discovering Fine-grained Sentiment in Suicide Notes. Biomedical Informatics Insights, vol. 5 (Suppl. 1) pp. 137-145, 2012
- Community dynamics in the social networks
D. Fuhry, Y. Ruan and S. Parthasarathy. Local/Global Term Analysis for Discovering Community Differences in Social Networks. 4th Int'l ACM Conference on Web Science (WebSci), 2012.
D. Fuhry. Summarization, Search, and Community Analysis in Social Networks. Doctoral Symposium at NSF SoCS Symposium, 2012.
- Multi-faceted social media analytics platform: Spatio-Temporal-Thematic, People-Content-Network
and Sentiment-Emotion-Subjectivity analyses
H. Purohit and A. Sheth. Twitris v3: From Citizen Sensing to Analysis, Coordination and Action. ICWSM 2013, Demo track. (to appear)
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.
- Dynamics of content driven networks
S. Parthasarathy. A Scalable Framework for Content+Network Analytics. NSF SoCS Symposium, 2012.
- Graph sparsification for community detection
V. Satuluri, S. Parthasarathy, Y. Ruan. Local Graph Sparsification for Scalable Clustering. ACM SIGMOD Int’l Conference on Management of Data, 2011.
Y. Ruan, D. Fuhry, S. Parthasarathy. Efficient Community Detection in Large Networks using Content and Links. In preparation.
- Social media content compression
X. Yang, A. Ghoting, Y. Ruan, S. Parthasarathy. A Framework for Summarizing and Analyzing Twitter Feeds. ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining, 2012.
Foundation of SOCS:
Checkout summaries for related research and projects here
Contact: Hemant Purohit