Social Media Attention to Geopolitical Conflicts - An Analysis of Weibo Users' Comments on the Israeli-Palestinian Conflict
Bibliographic record
Abstract
In the globalized information age, social media has become a primary channel for news and information dissemination, particularly during geopolitical conflicts. This study investigates public sentiment and discourse on the Israeli-Palestinian conflict on the Chinese social media platform Weibo. Utilizing content analysis, the researcher conducted sentiment statistics and word frequency analysis on Weibo comments to understand Chinese public attitudes toward this conflict. The research reveals a significant increase in negative emotions from 33.33% in October 2023 to 100% in April 2024, indicating growing public discontent and concern as the conflict intensified. Concurrently, positive emotions sharply declined from 47.62% to 0%, reflecting diminished hopes for a peaceful resolution. Neutral sentiments also fluctuated, initially at 19.05%, dropping to 0% by April 2024. Additionally, the study identifies a shift in keyword usage from "world peace" and "hope" to specific entities like "Israel" and "Hamas," and terms like "disaster." This highlights a change in public and media focus from peace initiatives to the humanitarian impact of the conflict. Understanding these dynamics is crucial for promoting a more inclusive gaming environment, challenging existing gender stereotypes, and fostering social stability. This research contributes to a deeper understanding of Chinese public opinion on international geopolitical issues and underscores the importance of social media in shaping public discourse.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".