On the Fringe - Analysis of Violent Discourse on Ideologically Motivated Violent Extremism on Less Regulated Social Media Platforms
Bibliographic record
Abstract
Smaller-scale, less-regulated social media platforms are increasingly exploited and misused for sharing and communicating harmful and violent ideological grievances.In some instances, these platforms are used to facilitate and express support for real-world acts of violence inspired by ideologically motivated violent extremist (IMVE) grievances.These platforms provide unique user affordances, such as anonymity, that generates online environments for IMVE to flourish.The following paper aims to better understand the role of smaller-scale, less-regulated platforms in facilitating a hospitable environment for IMVE narratives, messaging, and activity.In particular, the paper investigates the trajectory of violent discourse to understand how and why online users radicalize to violence.To investigate this trajectory, the research is guided by three questions: (1) How does the trajectory of violent discourse manifest on smaller-scale, lessregulated social media platforms?( 2) What is the level of user engagement (i.e., comments) with violent content inspired by ideologically motivated violent extremism?and, (3) How does the user engagement impact the trajectory of violent discourse on the platform?Using four original datasets built using open-source intelligence from 4chan's /pol/ board, the paper provides analysis that better situates our understanding of the impact of online violent discourse in inciting and inspiring acts of real-world violence.The findings also identify the daily prevalence of hateful and violent rhetoric posted to 4chan and trends in user engagement with violent discourse.In doing so, the paper confirms an echo-chamber of violent discourse is constructed and fostered within the platform and presents a legitimate risk for inspiring or inciting real-world acts of violence.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| 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".