Measuring Online Emotional Reactions to Events

TL;DR

This paper introduces a method combining change point detection and transformer-based topic modeling to systematically measure and explain emotional reactions to offline events using social media data, aiding real-time social monitoring.

Contribution

It presents a novel approach that integrates change point detection with topic modeling to analyze emotional responses to events in social media data.

Findings

Effective detection of emotional reaction shifts during social crises

Disaggregation of topics reveals nuanced emotional and moral responses

Method applied successfully to COVID-19 related social change data

Abstract

The rich and dynamic information environment of social media provides researchers, policy makers, and entrepreneurs with opportunities to learn about social phenomena in a timely manner. However, using this data to understand social behavior is difficult due 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 on a corpus of tweets from a large US metropolitan area between January and August, 2020, covering a period of great social change. We demonstrate that our method is able to disaggregate topics to measure population's emotional and moral reactions. This capability allows for better monitoring of population's reactions during crises using online data.