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.

翻译：2024年，日本严重的稻米短缺引发了新闻媒体与社交平台上的广泛公众争议，最终形成了所谓的“令和米骚动”。本研究以“令和米骚动”为案例，提出了一个分析框架，用于探究X（原Twitter）与新闻报道中所表达情绪的时间动态与因果交互关系。尽管近期研究表明，社交媒体与大众媒体之间的情绪会相互影响，但此类情绪转变的模式与传播路径仍未得到充分理解。为弥补这一空白，我们应用了基于普拉奇克八种基本情绪的机器学习情感分类方法，对X平台帖文与国内新闻报道进行了分析。研究发现，X平台上的情绪转变与信息传播均早于新闻媒体。此外，在这两类媒体平台上，恐惧最初是最为主导的情绪，但随着时间的推移，其与希望情绪交织，最终希望成为主导情绪。我们的研究结果表明，社交媒体上的情绪表达模式或许可作为探究更广泛社会动态的一个视角。