Exposure to Extremist Online Content Could Lead to Violent Radicalization:A Systematic Review of Empirical Evidence
Bibliographic record
Abstract
The main objective of this systematic review is to synthesize the empirical evidence on how the Internet and social media may, or may not, constitute spaces for exchange that can be favorable to violent extremism. Of the 5,182 studies generated from the searches, 11 studies were eligible for inclusion in this review. We considered empirical studies with qualitative, quantitative, and mixed designs, but did not conduct meta-analysis due to the heterogeneous and at times incomparable nature of the data. The reviewed studies provide tentative evidence that exposure to radical violent online material is associated with extremist online and offline attitudes, as well as the risk of committing political violence among white supremacist, neo-Nazi, and radical Islamist groups. Active seekers of violent radical material also seem to be at higher risk of engaging in political violence as compared to passive seekers. The Internet’s role thus seems to be one of decision-shaping, which, in association with offline factors, can be associated to decision-making. The methodological limitations of the reviewed studies are discussed, and recommendations are made for future research.
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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 | 0.012 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".