Abstract

The rich and dynamic information environment of social media provides researchers, policymakers, and entrepreneurs with opportunities to learn about social phenomena in a timely manner. However, using these data to understand social behavior is difficult due to heterogeneity of topics and events discussed in the highly dynamic online information environment. To address these challenges, we present a method for systematically detecting and measuring emotional reactions to offline events using change point detection on the time series of collective affect, and further explaining these reactions using a transformer-based topic model. We demonstrate the utility of the method by successfully detecting major and smaller events on three different datasets, including (1) a Los Angeles Tweet dataset between Jan. and Aug. 2020, in which we revealed the complex psychological impact of the BlackLivesMatter …

Date

January 11, 2024

Authors

Siyi Guo, Zihao He, Ashwin Rao, Fred Morstatter, Jeffrey Brantingham, Kristina Lerman

Journal

ACM Transactions on the Web

Publisher

ACM