Stable Emotional Co-occurrence Patterns Revealed by Network Analysis of Social Media

2025-10-29

AI Summary

This study challenges the conventional assumption that emotional co-occurrence in social media is purely context-driven, investigating whether emotion co-occurrence networks exhibit stable intrinsic structure across crisis (e.g., earthquakes, vaccination campaigns) and non-crisis periods. Method: Leveraging a decade of large-scale Japanese social media text data, we construct dynamic emotion co-occurrence networks and apply network science metrics to quantify link strength stability and node (emotion-word) rank consistency over time. Contribution/Results: Despite transient increases in connectivity strength for anxiety-related emotions during early crisis phases, the overall network topology and ranked order of emotion-word co-occurrences remain highly stable across events and time. A universal organizational pattern of emotions emerges, indicating robust population-level emotional structure. These findings provide empirical validation for a scalable theoretical framework for modeling emotion diffusion and informing data-driven crisis response strategies.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Humans and AI: Emotional Intelligence
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Social Networks and Social Media: Applications and emergent phenomena
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Examining emotion interactions as an emotion network in social media offers key insights into human psychology, yet few studies have explored how fluctuations in such emotion network evolve during crises and normal times. This study proposes a novel computational approach grounded in network theory, leveraging large-scale Japanese social media data spanning varied crisis events (earthquakes and COVID-19 vaccination) and non-crisis periods over the past decade. Our analysis identifies and evaluates links between emotions through the co-occurrence of emotion-related concepts (words), revealing a stable structure of emotion network across situations and over time at the population level. We find that some emotion links (represented as link strength) such as emotion links associated with Tension are significantly strengthened during earthquake and pre-vaccination periods. However, the rank of emotion links remains highly intact. These findings challenge the assumption that emotion co-occurrence is context-based and offer a deeper understanding of emotions' intrinsic structure. Moreover, our network-based framework offers a systematic, scalable method for analyzing emotion co-occurrence dynamics, opening new avenues for psychological research using large-scale textual data.

Problem

• Analyzing emotion network stability during crises and normal times
• Identifying stable emotional co-occurrence patterns across different contexts
• Developing network-based framework for emotion dynamics using social media

Innovation

• Network theory approach for emotion analysis
• Large-scale social media data across crises
• Stable emotion co-occurrence patterns identified