Differentiating Online Posting Behaviors of Violent and Nonviolent 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 detect and assess online posting behaviors of violent extremists prior to their engagement in violence offline, but little is empirically known about their online behaviors generally or the differences in their behaviors compared with nonviolent extremists who share similar ideological beliefs particularly. In this study, we drew from a unique sample of violent and nonviolent right-wing extremists to compare their posting behaviors in the largest White supremacy web-forum. We used logistic regression and sensitivity analysis to explore how users’ time of entry into the lifespan of an extremist sub-forum and their cumulative posting activity predicted their violence status. We found a number of significant differences in the posting behaviors of violent and nonviolent extremists which may inform future risk factor frameworks used by law enforcement and intelligence agencies to identify credible threats online.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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