Psychopaths Online: The Linguistic Traces of Psychopathy in Email, Text Messaging and Facebook
Bibliographic record
Abstract
Individuals high in psychopathy are interpersonally manipulative, exhibit callous affect, and have criminal tendencies. The present study examines whether these attributes of psychopathy are correlated with linguistic patterns present in everyday online communication. Participants’ emails, SMS messages, and Facebook messages were collected and analyzed in relation to their scores on the Self-Report Psychopathy Test III. The findings suggest that psychopathic tendencies leave a trace in online discourse, and that different forms of online media sometimes moderate the association between a linguistic dimension and psychopathy scores. Consistent with previous studies and the emotional and interpersonal deficits central to psychopathy, participants higher in psychopathy showed more evidence of psychological distancing, wrote less comprehensible discourse, and produced more interpersonally hostile language. The results reveal that linguistic traces of psychopathy can be detected in online communication, and that those with higher traits of psychopathy fail to modify their language use across media types.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".