Internet trolling and everyday sadism: Parallel effects on pain perception and moral judgment
Bibliographic record
Abstract
OBJECTIVE: This research seeks to clarify the association between online trolling and sadistic personality, and to provide evidence that the reward and rationalization processes at work in sadism are likewise manifest in online trolling. METHOD: Online respondents (community adults and university students; total N = 1,715) completed self-report measures of personality and trolling behavior. They subsequently engaged in one of two judgment tasks. In Study 1, respondents viewed stimuli depicting scenes of emotional/physical suffering and provided ratings of (a) perceived pain intensity and (b) pleasure experienced while viewing the photos. In Study 2, the iTroll questionnaire was developed and validated. It was then administered alongside a moral judgment task. RESULTS: Across both studies, online trolling was strongly associated with a sadistic personality profile. Moreover, sadism and trolling predicted identical patterns of pleasure and harm minimization. The incremental contribution of sadism was sustained even when controlling for broader antisocial tendencies (i.e., the Dark Triad, callous-unemotionality, and trait aggression). CONCLUSIONS: Results confirm that online trolling is motivated (at least in part) by sadistic tendencies. Coupled with effective rationalization mechanisms, sadistic pleasure can be consummated in such everyday behaviors as online trolling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".