The Location of Maximum Emotion in Deceptive and Truthful Texts
Bibliographic record
Abstract
Meta-analytic evidence suggests that verbal patterns of emotion betray deceit, but it is presently unclear whether the location of maximum emotion in lies and truths matters to reveal deception. We contribute to the deception literature by offering analyses at the sentence level to locate where emotion is most pronounced in deceptive versus truthful texts. Using two public data sets—news articles (Study 1) and hotel reviews (Study 2)—we found that maximum emotion occurs toward the beginning of deceptive texts while maximum emotion appears later for truthful texts. In addition to demonstrating the effect across diverse settings, we used two different measurements for emotion and separated the results by valence, replicating the maximum emotion effect each time. The predictive nature of maximum affect ranged from 54% to 56% across data sets, a rate consistent with most deception studies using 50-50 lie–truth base rates. Implications for future research and deception theory are discussed.
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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 | 0.009 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".