The internet, and social media in particular, have made individual and institutional discourse visible like never before. Yet the mechanisms that shape the production of discourse — what leads individuals or institutions to speak up, whom do they address, what do they say — is not yet well understood. My research focuses on understanding these dynamics through the quantitative aggregation of collective communication behavior. In particular, my approach emphasizes the role that accountability, credibility, and legitimacy within social networks and communities play in shaping observable discourse.
SOME TOPICS OF INTEREST:
-- The social structural conditions that encourage reason-based responses to facts and criticism
-- The semantic signatures of coordinated efforts to manipulate audiences or obscure facts
-- The evolution of conversations in response to unpredictable events or shocks
Because my research focuses on the role of social pressure from potentially large communities, my methods emphasize natural experiments and other treatments where behavior can be observed in its natural social context and where its consequences are real.
- Margolin, D., & Liao, W. The Emotional Antecedents of Solidarity in Social Media Crowds.
- Margolin, D., & Markowitz, D. A Multi-Theoretical Approach to Big Text Data: Comparing Expressive and Rhetorical Logics in Yelp Reviews. Communication Research.
- Margolin, D., Hannak, A., & Weber, I. Political Fact-Checking on Twitter: When Do Corrections Have an Effect? Political Communication.
- Lin, Y., Wen, X., & Margolin, D. Tracking and Analyzing Individual Distress Following Terrorist Attacks Using Social Media Streams. Risk analysis : an official publication of the Society for Risk Analysis.
- Newell, E., Schang, A., Margolin, D., & Ruths, D. (2017). Assessing the Verifiability of Attributions in News Text. Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers).