Bystanders Join in Cyberbullying on Social Networking Sites: The Deindividuation and Moral Disengagement Perspectives
Bibliographic record
Abstract
Bystanders Join in Cyberbullying on Social Networking Sites: The Deindividuation and Moral Disengagement Perspectives Cyberbullying on social networking sites escalates when bystanders join in the bullying. Bystanders’ joining-in behaviors reinforce the abuse, expose victims to a larger audience, and encourage further abuse by signaling their approval of the aggressive behavior. This study developed an integrative model that explains bystanders’ joining-in cyberbullying behaviors on SNSs to offer actionable insights into reducing such harmful behaviors. We tested the model using 1,179 responses using a scenario survey study. Our findings suggest that IT artifacts (including digital profile, search and privacy, relational ties, and network transparency) activated two key mechanisms that lead to cyberbullying joining-in behaviors: (i) the deindividuation experiences that attenuate self-identity and put salience on group/social identity, and (ii) the moral disengagement practices that permit the exercise of cognitive maneuvers to justify group-interested choices that do not align with social standard. The findings explain why people who do not know each other gang up to bully a target on social media. Platform owners who wish to discourage bystanders from joining in undesirable activities may consider regulating how users could share and access digital resources in a social network and should acknowledge the influence of social identity in igniting, driving, and prolonging harmful online group behaviors.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".