Sentiment Analysis of Twitter Users during the 12-Day Iran-Israel War: A Psychological Approach
Bibliographic record
Abstract
Background: Social networks, especially Twitter, have become an effective platform for reflecting the views of individuals and elites on world political and social events.The aim of this study was to psychologically analyze Twitter users' reactions to the 12-day Iran-Israel war (June 2025) with a focus on the constructs of action guides (HBM) and subjective norms (TPB).Materials: To ensure credibility and reduce noise, 3,367 tweets were selected from a total of 35,428 tweets posted by verified accounts between June 14 and 25 and analyzed using the fuzzy classifier Eclass1-MIMO.Results: The results indicated that 44% of the tweets were negative, 46% were neutral, and only 10% were positive.The findings showed that regions with a history of social and historical tensions exhibited the highest proportion of negative sentiments, while positive tweets often contained Cues to Action, which were shared 2.3 times more frequently.Additionally, tweets based on Subjective Norms, emphasizing social acceptance or rejection, achieved the highest engagement rates. Conclusion:The study highlights the critical role of verified influencers in shaping public sentiment and the potential dangers of unchecked information flow in polarized environments.It recommends that media policymakers and platforms enhance their content verification mechanisms, issue clear warnings on risky content, and promote messages aligned with constructive norms to counter misinformation and foster public trust in times of crisis.
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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 | 0.017 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".