A Bibliometric Analysis of Radicalization through Social Media
Bibliographic record
Abstract
The purpose of this study is to synthesize the literature relating to radicalization on social media, a space with enhanced concerns about nurturing propaganda and conspiracies for violent extremism. Through the systematic review of 82 peer-reviewed studies related to radicalization through social media published in scholarly journals, this paper evidence the growth of robust studies on the usage of social media for radicalization. Nonetheless, the current work hardly discusses the radicalization issues through social media and reveals an increasing trend of publication from 2017 with a major contribution from the USA, Germany, and England. The thematic analysis indicated determinants of radicalization and the mitigation measures for the deradicalization of content on social media. However, the knowledge gap persists to understand the effects of radicalization in the different regional settings and further framing of content specific to target populations. Individuals must have the critical social media literacy to counteract the rising radicalization through social media. Individual users’ political interests are key factors in their radicalization such as citizens losing faith in the government and political parties. Active rather than passive searchers of violent radical material are more likely to engage in political violence. The results indicate that further research using experimental design, grounded theory, and pilot interventions may be relevant to suggest a solution to mitigate radicalization on social media.
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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 | 0.017 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.200 | 0.250 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".