Temporal Shifts and Causal Interactions of Emotions in Social and Mass Media: A Case Study of the"Reiwa Rice Riot"in Japan

2026-02-15

AI Summary

This study investigates the temporal dynamics and causal interactions between social media and mainstream media in the emotional dissemination surrounding public events. Focusing on Japan’s “Reiwa Rice Riots” as a case study, the research integrates data from X (formerly Twitter) and news media outlets, employing a machine learning classifier grounded in Plutchik’s eight basic emotions, complemented by time-series and Granger causality analyses. The findings reveal that emotional expressions on social media significantly precede those in news coverage, initially dominated by fear and subsequently shifting toward hope. Moreover, social media sentiment demonstrates predictive power over the emotional trajectory of mainstream reporting, highlighting its potential as an early indicator of societal affect. This work provides empirical evidence and methodological innovation for understanding cross-media emotional evolution.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• Social Networks and Social Media: Computational social science
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Social recommender systems and personalization for the social Web

Abstract

In Japan, severe rice shortages in 2024 sparked widespread public controversy across both news media and social platforms, culminating in what has been termed the"Reiwa Rice Riot."This study proposes a framework to analyze the temporal dynamics and causal interactions of emotions expressed on X (formerly Twitter) and in news articles, using the"Reiwa Rice Riot"as a case study. While recent studies have shown that emotions mutually influence each other between social and mass media, the patterns and transmission pathways of such emotional shifts remain insufficiently understood. To address this gap, we applied a machine learning-based emotion classification grounded in Plutchik's eight basic emotions to analyze posts from X and domestic news articles. Our findings reveal that emotional shifts and information dissemination on X preceded those in news media. Furthermore, in both media platforms, the fear was initially the most dominant emotion, but over time intersected with hope which ultimately became the prevailing emotion. Our findings suggest that patterns in emotional expressions on social media may serve as a lens for exploring broader social dynamics.

Problem

• emotional dynamics
• social media
• mass media
• causal interaction
• temporal shifts

Innovation

• emotion dynamics
• causal interaction
• machine learning
• Plutchik's emotion model
• social media vs. news media