Bystanders Join in Cyberbullying on Social Networking Sites: The Deindividuation and Moral Disengagement Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it