Challenges in automated deception detection in computer‐mediated communication
Bibliographic record
Abstract
Abstract Deception detection remains novel, challenging, and important in natural language processing, machine learning, and the broader LIS community. Computational tools capable of alerting users to potentially deceptive content in computer‐mediated messages are invaluable for supporting undisrupted, computer‐mediated communication, information seeking, credibility assessment and decision making. The goal of this ongoing research is to inform creation of such automated capabilities. In this study we elicit a sample of 90 computer‐mediated personal stories with varying levels of deception. Each story has 10 associated human judgments, confidence scores, and explanations. In total, 990 unique respondents participated in the study. Three analytical approaches are applied: human judgment accuracy, linguistic cue detection, and machine learning. Comparable to previous research results, human judges achieve 50–63% success rates. Actual deception levels negatively correlate with their confident judgments as being deceptive (r= −0.35, df=88, p =0.008). The best‐performing machine learning algorithms reach 65% accuracy. Linguistic cues are extracted, calculated, and modeled with logistic regression, but are found not to be significant predictors of deception level or confidence score. We address the associated challenges with error analysis of the respondents' stories, and prose a faceted deception classification (theme, centrality, realism, essence, distancing) as well as a typology for stated perceived cues for deception detection (world knowledge, logical contradiction, linguistic evidence, and intuitive sense).
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How this classification was reachedexpand
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 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".