Sentiment Analysis of YouTube Comments on Videos about Severe Extremist Attacks
Bibliographic record
Abstract
The rise of extremist attacks, along with the growing prevalence of online discourse, has led to intense discussions on platforms like YouTube. This study examines the sentiment and emotional responses in YouTube comments related to five of the most severe extremist attacks between 2018 and 2024. Using natural language processing (NLP) techniques, a dataset of 96 videos and 72,150 comments was analyzed to uncover patterns in public discourse. Sentiment analysis and emotion analysis were performed with two machine learning models, revealing that negative sentiment (38.6%) was the most prevalent, followed by positive (31.2%) and neutral (30.3%) sentiment. Emotion analysis showed that anger (18.3%) and disgust (11.5%) were dominant emotions, highlighting the charged nature of these discussions. Comparing sentiment and emotions across different attacks revealed notable variations, suggesting that the characteristics and nature of an attack can have an influence on public reactions. These findings contribute to a broader understanding of how online communities engage with extremist events and the YouTube videos associated with them.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".