The Pulse of Mood Online: Unveiling Emotional Reactions in a Dynamic Social Media Landscape

TL;DR

This paper introduces a method combining change point detection and transformer-based topic modeling to identify and analyze emotional reactions to offline events in social media data, enabling timely insights into public sentiment during crises.

Contribution

The authors develop a novel approach that systematically detects emotional reactions to offline events in social media using change point detection and explains them with transformer-based topic models.

Findings

Successfully detected major and minor events across three datasets.

Revealed complex psychological impacts of social movements and crises.

Uncovered emotional and moral shifts before and after key events.

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 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 movement and the COVID-19 pandemic, (2) a dataset related to abortion rights discussions in USA, in which we uncovered the strong emotional reactions to the overturn of Roe v. Wade and state abortion bans, and (3) a dataset about the 2022 French presidential election, in which we discovered the emotional and moral shift from positive before voting to fear and criticism after voting. The capability of our method allows for better sensing and monitoring of population's reactions during crises using online data.