Exploring Temporal and Multilingual Dynamics of Post-Disaster Social Media Discourse: A Case of Fukushima Daiichi Nuclear Accident
Bibliographic record
Abstract
The 2011 Fukushima Daiichi nuclear disaster has led to worldwide disruptive discussions related to crisis. In April 2021, the news that the Japanese Cabinet decided to discharge the stored wastewater into the Pacific Ocean drew global attention once again. Social media platforms like Twitter are ubiquitously used to gain information and exchange opinions during and after a crisis. Analyzing crisis-related tweets can help capture insights for public situational awareness development, crisis global response coordination, and post-disaster policy-making. We examined corresponding Twitter discourse in different languages about the nuclear disaster in 2011 and the follow-up discharge of the stored water until 2021. We utilized NLP techniques including topic modeling and sentiment analysis to identify the dominant topics related to the nuclear disaster, the post-disaster discourses, and the public attitudes towards these topics in different time phases. Our work revealed multilingual disparities of post-disaster discourse dynamics and the regional public attitudes towards the post-disaster management in the long run.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".