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.
Alex: Welcome to another episode of ResearchPod.
Sam: Today, we're looking at how emotions spread during a real-world crisis—specifically, a rice shortage in Japan that got people really worried. This discussion centers on a study by Erina Murata and colleagues from the University of Tokyo, examining the "Reiwa Rice Riot" in 2024.
Alex: What caused the shortage?
Sam: Bad weather cut harvests, and higher wheat prices from the war in Ukraine made more people buy rice as a staple food. That sparked public anxiety and confusion on social media and in news articles. The key claim is that emotional reactions on X—formerly Twitter—shifted and spread before they did in news media.
Alex: So this paper is basically asking whether social media now leads the way emotions play out in crises, instead of news calling the shots like it used to?
Sam: Yes, that's the core puzzle. Traditionally, news media shapes what people feel by deciding what to cover and repeat. But the study suggests social media, especially X, took the lead here. Emotions like fear surged first on X, then showed up in news coverage.
Alex: And rice is such a big deal in Japan culturally—like a trigger for unrest in the past?
Sam: Exactly. The shortage led to panic buying reports, price worries, and confusion about supply, all playing out publicly from August to September 2024. They collected over 156,000 original X posts mentioning "rice shortage," plus about 1,000 news articles from Japanese sources. Both peaked in activity, but X hit its high first, by several days.
Alex: So posts on X ramped up mid-August with rumors and on-the-ground talk, and news followed about ten days later with fact-checks?
Sam: That's what the data shows. The study smoothed out daily ups and downs using 7-day moving averages—like averaging a week's scores to see the real trend without weekend dips messing it up. X activity peaked late August; news early September. This timing hints that everyday voices and rumors on X drove news outlets to respond.
Alex: The study digs into emotions specifically—why?
Feelings like anxiety drive how info spreads and amplifies. They looked at shifts from negative to more hopeful tones. On both platforms, fear started strong—think worry about empty shelves—but then anticipation took over, as people started expecting fixes like government action. Fear peaked mid-August on X, then anticipation crossed it and became dominant by early September. News followed the same path, but the switch happened later there.
Alex: Fear makes sense for shortages, but anticipation—like shifting to "okay, supplies might come back"?
Sam: Yes. This shows emotions don't stay stuck; they evolve as info updates, and X moved quicker. To capture those eight basic feelings—fear, anger, sadness, surprise, anticipation, joy, disgust, trust—they used a setup based on psychologist Robert Plutchik's model. It's like primary colors for emotions: these eight mix and build like red and yellow make orange, giving a fuller picture than just "positive or negative."
Alex: How did they measure those?
Sam: They trained a machine learning tool on Japanese tweets labeled with those emotions at different strengths, so it could score texts reliably. The tool reads posts or articles, estimates probabilities for each of those eight emotions, then averages daily scores into trends. It got about 75% right on test data. This let them plot how fear dominated early, then faded as anticipation rose—smoothed over seven days for steady trends.
Alex: And these emotion trends peaked earlier on X too, matching the post counts?
Sam: Yes. Fear topped out mid-August on X; anticipation overtook it sooner there than in news. Both media calmed by late September. The study suggests X captured raw public reactions—like panic buying reports—faster, pulling news along.
Alex: Does that mean social media caused the news emotions, or just happened first by chance?
Sam: To check direction—not just timing—they measured transfer entropy. Imagine two rivers: one way to tell if upstream flow shapes downstream currents is seeing if knowing the upstream history predicts the downstream better than its own past alone. Here, it showed X emotions reduced uncertainty in forecasting news emotions, especially for fear and anger. Transfer entropy from X to news beat the reverse for all eight emotions overall. Negative ones like fear, anger, disgust flowed strongest from X to news—meaning X surges got reflected in news tone.
Alex: Like X's fear surge made news' next feelings more predictable, proving it led the way?
Sam: Exactly. In August, when crisis hit hardest, X drove most emotions; September saw some flip as news deepened analysis. The evidence points to social media as the starting point for emotional spread in this case, inverting the old mass media lead.
Alex: You've got the trends and the direction measure. But since it's just X, not other apps, does that limit seeing the full picture?
Sam: The paper notes that—sticking to one platform risks missing broader chatter on places like Instagram. Transfer entropy spots info flow, not airtight cause, especially if outside events like government announcements hit both simultaneously. Still, for this riot, it clearly showed X precedence.
Alex: Well, it suggests monitoring social emotions could spot crisis waves early, before news amplifies them. A meaningful shift in how we think about media influence. Thanks, Sam—that's a clear window into how feelings drive crises now. Listeners, check the show notes for the full paper.
Sam: Indeed. The study positions social media as a leading indicator of emotional dynamics, with tools to quantify it. Useful for understanding public discourse in volatile times. Thanks for listening to ResearchPod.