“Help me please, I need practical advice”: A qualitative exploration of social support dynamics among incels on online forums
Bibliographic record
Abstract
The incel population comprises men experiencing involuntary celibacy who mingle based on their challenges in establishing romantic connections. Despite the current issues plaguing incel forums (e.g., violent content), they were originally conceived as platforms for social support to alleviate loneliness among sexually inexperienced individuals. However, documentation of support types within these forums is limited. The aim of this study was to document the exchange of social support within incel forums, utilizing Braithwaite et al.’s (1999) adaptation of Cutrona and Suhr’s (1992) social support typology (informational, emotional, esteem, network, tangible support) to analyze the forms of support exchanged within incels forums. Thematic analyses of 37 threads from r/IncelExit (i.e., users seeking to leave inceldom) and Incels.is (i.e., users deeply entrenched in inceldom) reveal a prevalence of informational support, followed by emotional support. These findings align with existing literature positing that informational support is the prevailing type in online interactions on forums between strangers, where anonymity also facilitates the intimate exchanges characteristic of emotional support. Notable distinctions emerge between the two forums: r/IncelExit favoured informational support (prioritized when the problem is perceived as controllable) to suggest concrete actions to resolve the problem, while Incels.is emphasized emotional support (prioritized when the problem is perceived as uncontrollable) to show empathy and understanding of the situation. Our findings also reveal that antisocial support (i.e., encouraging self-destructive behaviours instead of providing genuine support) was sometimes present, especially on Incels.is. This study provides an initial exploration of support dynamics in incels forums, with implications for interventions.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".