Why do People Disseminate Fictitious Accounts? A Systematic Analysis of snopes.com
Bibliographic record
Abstract
People often disseminate fictitious information and contrived anecdotes, some of which can be destructive. Thispaper explores the proposition that most, if not all, fictitious information can be classified into four clusters.Each cluster reinforces one of four underlying determinants of positive emotions—unambiguous duties, moralauthorities, extensive capabilities, and stable values over time. This framework is derived from socio-emotionalselectivity theory, self-discrepancy theory, and the meaning maintenance model. To assess these propositions,1500 fictitious claims, derived from snopes.com, were subjected to thematic analysis. To code these claims, allnouns and verbs were translated to broader categories. Then, researchers sorted these abstracted claims into 88piles of overlapping accounts. These 88 accounts were next sorted into 19 broader piles, each reflecting a distincttheme. All 19 themes aligned to one of the four underling determinants of positive emotions. These findingsindicate that, arguably, the need to curb negative emotions and to foster positive emotions motivates these biasedand fictitious accounts. The findings also highlight several distinct avenues in which each of these four needs canbe fulfilled. The implications of these findings to a range of issues, from violence and mental illness toadvertising and marketing, 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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".