This seed grant is instrumental in establishing interdisciplinary research and education activities among computer science, political science, and mass communication researchers in mining big heterogeneous data generated at a rapid rate from social media, state news sources, and web sites to answer many unanswered political science and mass communication questions. To date, we have been focusing on understanding policy diffusion, social network models of policy connectedness across state lines, the role of social media in influencing state politics and policy, and micro-targeting strategies in election campaigns. The study requires automated analysis of social media data and efficient management and retrieval of the data. For educational activities, we focus on new interdisciplinary courses at the intersection of the three disciplines.
Research Outcomes: 
Automated analysis for tweets with policy agendas
- Classifier for major policy agenda topics in congressional bills
Educational Outcomes:
- Computational Communication: Creative Advertising, first offered in Spring 2015
Funding Organization: College of Liberal Arts and Sciences
Duration: 07/01/2014 to 06/30/2017
Award Amount: $247,719.00
Principal Investigator(s): Wallapak Tavanapong (Computer Science), David Peterson (Political Science), Gang Han (Journalism and Communication), Jan Lauren Boyles (Journalism and Communication), Johnn Wong (Computer Science), Ying Cai (Computer Science), Wensheng Zhang (Computer Science), Michael J Bugeja (Journalism and Communication), Michael F Dahlstrom (Journalism and Communication), Daniela Dimitrova (Journalism and Communication), Jay Newel (Journalism and Communication), David Andersen (Political Science), Tessa Ditonto (Political Science), Mack Shelley (Political Science)