Role of Entertainment, Social Goals, and Accuracy Concerns in Knowingly Spreading Questionable Brand Rumors
Bibliographic record
Abstract
Accuracy goals are central to communication theory. Consistent with this perspective, a comprehensive review suggests that rumors are spread largely for accuracy reasons—either because transmitters, in fact, believe the rumors are true or for the purpose of verification through sense-making (DiFonzo and Bordia 2007). This literature also suggests that rumors can be spread in service of social goals such as affiliation (Rosnow 1991), for instance, by passing on social rumors about disliked out-groups to strengthen ties with the in-group. Our own research focused on brand rumors and suggests that entertainment is a common reason that such rumors are shared. Moreover, we show entertaining rumors serve social affiliation goals, and that the social benefits of spreading entertaining rumors can dominate private concerns about their inaccuracy. Social goals also led consumers to embellish the rumors they spread in order to make them more entertaining and to share rumors over factual brand information. These entertainment effects are shown to be independent of any alternative sense-making or affect sharing explanations for transmitting questionable rumors. Theoretically speaking, the entertainment effects identified here offer a novel explanation for the spread of questionable or implausible rumors. That is, we show consumers will knowingly spread implausible rumors just because they offer a good story for entertaining others. This idea has important practical implications for brand strategies dealing with misleading rumors.
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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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".