Revenge via social media and relationship contexts: Prevalence and measurement
Bibliographic record
Abstract
Social media platforms’ unique characteristics may make them particularly good outlets for getting even with relational partners. Establishing the prevalence of social media revenge and identifying the forms such revenge may take in different relationship contexts is an important first step in broadening our understanding of these behaviors. In a mixed-methods study, undergraduates ( N = 732) and community members ( N = 124) were randomly assigned to one of four relational contexts (coworkers, family, friends, and romantic partners) and asked to describe an act of social media revenge experienced or observed in their assigned context. They then rated how often they were the avenger, target, and observer of five control and monitoring and 11 direct aggression behaviors adapted from the Cyber Dating Abuse Questionnaire. The prevalence of social media revenge across all relationship contexts, roles, and revenge types was low and participants reported observing social media revenge more frequently than being the target or avenger. Social media revenge was also more prevalent in some relationships than others and the type of relationship between avenger and target may have implications for how revenge is executed. Analysis of participants’ accounts identified novel revenge behaviors and suggested ways to improve measurement of social media revenge.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".