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Record W4408997505 · doi:10.2196/75167

Defining Cyberpsychopathy : An Integrative Review.

2025· article· en· W4408997505 on OpenAlexaff
Alexandre Hudon, Emmy Harvey, Sandrine Nicolas, Mathieu Dufour, Caroline Guérin-Thériault, Julie Bérubé-Fortin, Isabelle Combey, Yu Chen Yue, Antoine Perreault, Stéphanie Borduas Pagé, Véronique MacDermott

Bibliographic record

VenuePubMed · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversité de MontréalInstitut universitaire en santé mentale de MontréalDouglas Mental Health University InstituteInstitut Universitaire en Santé Mentale de QuébecInstitut national de psychiatrie légale Philippe-Pinel
Fundersnot available
KeywordsIntegrative medicinePsychologyMedicineAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The rapid expansion of digital communication platforms has created new spaces for the expression of antisocial, manipulative, and emotionally detached behaviors. While traditional psychopathy has been well-characterized in clinical and forensic settings, the manifestation of similar traits in digital environments, referred to as cyberpsychopathy, remains conceptually fragmented and underdefined. Although several studies have examined dark personality traits in relation to online aggression, trolling, and social media misuse, an integrative framework for understanding how psychopathic tendencies operate in virtual contexts has yet to be established. OBJECTIVE: The aim of this integrative review was to synthesize the existing literature on cyberpsychopathy in order to (1) identify the primary conceptual domains underpinning this construct, (2) assess the methodological quality of included studies, and (3) offer a preliminary, evidence-based definition of cyberpsychopathy that reflects both dispositional traits and digital affordances. METHODS: An integrative search of peer-reviewed literature was conducted using multiple databases to identify empirical studies published that explored psychopathy or dark personality traits in relation to online behaviors. Thirty-five studies met the inclusion criteria and were analyzed using thematic synthesis. The methodological quality of each study was evaluated. RESULTS: Six core conceptual domains were identified across the literature: (1) online behaviors (e.g., trolling, cyberbullying, deception), (2) online environment (e.g., anonymity, platform design, reward mechanisms), (3) sociodemographic factors (e.g., age, gender, culture), (4) personality traits (e.g., narcissism, Machiavellianism, psychopathy, sadism), (5) psychological factors (e.g., emotion dysregulation, impulsivity, low self-esteem), and (6) motivations (e.g., dominance, validation seeking, emotional compensation). These domains interact dynamically to shape the expression of psychopathic tendencies in online contexts. Most studies were of moderate to high quality, though methodological variability limited direct comparisons across findings. A working definition of cyberpsychopathy was proposed as a multidimensional construct involving the online expression of dark personality traits, shaped by digital affordances, psychological vulnerabilities, and social reinforcement mechanisms. CONCLUSIONS: Cyberpsychopathy represents a complex and context-dependent phenomenon that extends beyond traditional models of psychopathy. This review provides a foundational framework for its study and highlights the need for further empirical research, including the development of validated assessment tools tailored to digital behaviors. Understanding the mechanisms behind cyberpsychopathy is essential for designing effective interventions, informing platform moderation policies, and safeguarding user well-being in increasingly digital societies.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0140.010
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.018
GPT teacher head0.325
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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