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.
- Understanding community engagement in Twitter
- Influence analysis in evolving communities on social media
- Extracting diverse sentiment expression with target dependent polarity and emotion identification
- Community dynamics in the social networks
- Multi-faceted social media analytics platform: Spatio-Temporal-Thematic, People-Content-Network and Sentiment-Emotion-Subjectivity analyses
- Dynamics of content driven networks
- Graph sparsification for community detection
- Social media content compression
Foundation of SOCS: