Examining the Persisting and Desisting Online Posting Behaviors of Violent and Non-Violent Right-Wing Extremists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is an ongoing need for researchers, practitioners, and policymakers to identify and examine the online posting behaviors of violent extremists prior to their engagement in violence offline, but little is empirically known about their online presence generally or differences in their posting behaviors compared to their non-violent counterparts particularly. Even less is empirically known about their persisting and desisting posting patterns. This study drew from a unique sample of violent and non-violent right-wing extremists to examine online changes in posting patterns during the beginning, middle, and end of their observed posting activity. Here, we identified persister and desister posters to create four sample groups: non-violent persisters, non-violent desisters, violent persisters, and violent desisters. We then calculated the average number of posts for each sample group as well as quantified the existence of extremist ideologies and violent extremist mobilization efforts across each observed posting period. Overall, we identified several noteworthy posting patterns that may assist law enforcement and intelligence agencies in identifying credible threats online. We conclude with a discussion of the implications of the analysis, its limitations, and avenues for future research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it