Shades of hatred online: 4chan duplicate circulation surge during hybrid media events
Bibliographic record
Abstract
The 4chan /pol/ platform is a controversial online space on which a surge in hate speech has been observed. While recent research indicates that events may lead to more hate speech, empirical evidence on the phenomenon remains limited. This study analyzes 4chan /pol/ user activity during the mass shootings in Christchurch and Pittsburgh and compares the frequency and nature of user activity prior to these events. We find not only a surge in the use of hate speech and anti-Semitism but also increased circulation of duplicate messages, links, and images and an overall increase in messages from users who self-identify as “white supremacist” or “fascist” primarily voiced from English-speaking IP-based locations: the U.S., Canada, Australia, and Great Britain. Finally, we show how these hybrid media events share the arena with other prominent events involving different agendas, such as the U.S. midterm elections. The significant increase in duplicates during the hybrid media events in this study is interpreted beyond their memetic logic. This increase can be interpreted through what we refer to as activism of hate. Our findings indicate that there is either a group of dedicated users who are compelled to support the causes for which shooting took place and/or that users use automated means to achieve duplication.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".