Temporal shifts and relationships of emotions in social and mass media: A case study of the “Reiwa Rice Riot” in Japan

Abstract

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 directional relationships 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 are dynamically associated across social and mass media, the patterns and 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 suggest that emotional shifts on X tended to precede those in news media in a temporal sense. Furthermore, in both media platforms, fear was initially the most dominant emotion, but over time intersected with anticipation which ultimately became the prevailing emotion. Our findings suggest that patterns in emotional expressions on social media may serve as a lens for understanding temporal shifts in emotions during social crises and the temporal precedence of emotions across social and news media.