On deception and deception detection: Content analysis of computer‐mediated stated beliefs
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
Abstract Deception in computer‐mediated communication is defined as a message knowingly and intentionally transmitted by a sender to foster a false belief or conclusion by the perceiver. Stated beliefs about deception and deceptive messages or incidents are content analyzed in a sample of 324 computer‐mediated communications. Relevant stated beliefs are obtained through systematic sampling and querying of the blogosphere based on 80 English words commonly used to describe deceptive incidents. Deception is conceptualized broader than lying and includes a variety of deceptive strategies: falsification, concealment (omitting material facts) and equivocation (dodging or skirting issues). The stated beliefs are argued to be valuable toward the creation of a unified multi‐faceted ontology of deception, stratified along several classificatory facets such as (1) contextual domain (e.g., personal relations, politics, finances & insurance), (2) deception content (e.g., events, time, place, abstract notions), (3) message format (e.g., a complaint: they lied to us , a victim story: I was lied to or tricked , or a direct accusation: you're lying ), and (4) deception variety, each tied to particular verbal cues (e.g., misinforming, scheming, misrepresenting, or cheating). The paper positions automated deception detection within the field of library and information science (LIS), as a feasible natural language processing (NLP) task. Key findings and important constructs in deception research from interpersonal communication, psychology, criminology, and language technology studies are synthesized into an overview. Deception research is juxtaposed to several benevolent constructs in LIS research: trust, credibility, certainty, and authority.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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