When a Lie is More Believable than the Truth: The Dynamics of Lying and Discourse Analysis
Bibliographic record
Abstract
This article describes the preliminary findings of a research project that is investigating whether there are stable and consistent personality traits found in propaganda texts that indicate whether a statement disseminated via a media is truthful or not. It is not often the case that official government sources will clearly state whether a statement previously release was intended to be truthful or not. Even when a statement is shown to be false, the claim is usually made that it was unintentionally wrong or misleading. The project uses the Rumours Broadcasts targeting German troops and civilians in France, created by the British Political Warfare Executive from July 1942 to May 1945, consisting of over 200,000 words, to analyse the personality differences between true and false statements made in those broadcasts. The first question answered in the study is the amount of fictional versus non-fictional material is necessary to make a broadcast believable. This establishes a benchmark for determining consistent differences between true and false statements in a news release or broadcasts. The analysis is done using tools developed by IBM to examine the vast amounts of data created and displayed on various social media platforms in accordance with the Big Five Personality Traits Theory. While most of the personality traits identified by these tools do not reveal any significant differences, there are some, such as a lack of imagination, cautiousness, a lack of willingness to compromise, and worry, that show consistent significant differences between the fictional and non-fictional statements.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".