The Emergence of Violent Narratives in the Life-Course Trajectories of Online Forum Participants
Bibliographic record
Abstract
Online discussion forums have been identified as an online social milieu that may facilitate the radicalization process, or the development of violent narratives for a minority of participants, notably youth. Yet, very little is known on the nature of the conversations youth have online, the emotions they convey, and whether or how the sentiments expressed in online narratives may change over time. Using Life Course Theory (LCT) and General Strain Theory (GST) as theoretical guidance, this article seeks to address the development of negative emotions in an online context, specifically whether certain turning points (such as entry into adulthood) are associated with a change in the nature of sentiments expressed online. A mixed methods approach is used, where the content of posts from a sample of 96 individuals participating in three online discussion forums focused on Islamic issues is analyzed quantitatively and qualitatively to assess the nature and evolution of negative emotions. The results show that 1) minors have a wider range of sentiments than adults, 2) adults are more negative overall when compared to minors, and 3) both groups tended to become more negative over time. However, the most negative users of the sample did not show as much change as the others, remaining consistent in their narratives from the beginning to the end of the study period.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".