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

TL;DR

This study analyzes how emotions evolve and influence each other across social media and news during Japan's 2024 rice shortage, revealing social media emotions often precede news and highlighting fear and hope dynamics.

Contribution

It introduces a framework combining machine learning and emotion theory to analyze emotional shifts and causal interactions across media platforms during a social crisis.

Findings

Social media emotions precede news media in shifts.

Fear was initially dominant, later replaced by hope.

Emotional patterns reflect broader social dynamics.

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.