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Measuring Online Emotional Reactions to Events

Siyi Guo, Zihao He, Ashwin Rao, Eugene Jang, Yuanfeixue Nan, Fred Morstatter, Jeffrey Brantingham, Kristina Lerman

TL;DR

This work tackles measuring public emotional and moral reactions to offline events using social media data. It jointly develops change-point detection on transformer-derived affect time series and BERTopic-based topic explanations to identify and interpret reactions. Demonstrated on a large metropolitan Twitter corpus from January to August 2020, the method captures major events including the COVID-19 pandemic and racial justice protests, while disaggregating COVID-related discourse into subtopics for finer insight. The approach enables nuanced, multi-dimensional tracking of population responses with potential for real-time crisis monitoring and policy communication.

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.

Measuring Online Emotional Reactions to Events

TL;DR

This work tackles measuring public emotional and moral reactions to offline events using social media data. It jointly develops change-point detection on transformer-derived affect time series and BERTopic-based topic explanations to identify and interpret reactions. Demonstrated on a large metropolitan Twitter corpus from January to August 2020, the method captures major events including the COVID-19 pandemic and racial justice protests, while disaggregating COVID-related discourse into subtopics for finer insight. The approach enables nuanced, multi-dimensional tracking of population responses with potential for real-time crisis monitoring and policy communication.

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.
Paper Structure (12 sections, 4 figures, 1 table)

This paper contains 12 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods and Materials
  3. Data
  4. Emotion and Morality Detection
  5. Detecting and Measuring Change Points
  6. Explaining Changes with Topic Modeling
  7. Results
  8. Online Reactions to Offline Events
  9. Evaluation of the Proposed Pipeline
  10. Short-term and Long-term Changes in Affect
  11. Disentangling COVID-related Emotions
  12. Conclusion

Figures (4)

  • Figure 1: Pipeline to detect and measure online emotional reactions.
  • Figure 2: Time series of emotions and moral sentiments from January 1 to August 1, 2020. We show the daily fraction of tweets with different affect labels. The notable peaks and dips in the time series can be associated with the external events marked as vertical lines.
  • Figure 3: Short-term and long-term changes of emotions and moral sentiments around four events. The short-term change compares the peak/dip value after an event to the baseline level before the event. The long-term change compares the time series value around two weeks after the event to the baseline level.
  • Figure 4: Emotions and moral sentiments expressed in COVID-related topics during the two weeks after WHO announcement of the pandemic on 2020-03-11. The topics are COVID ("coronavirus, corona, virus"); grocery panics ("grocery, groceries, shelves", "water, dasani, hydro", and "toilet, paper, rolls"); leisure activities ("episode, episodes, show", "cook, cooking, cookout","tickets, ticket, selling"); and education ("teachers, students, learning", "schools, lausd, classes", "schools, lausd, closed").