A false image of health: how fake news and pseudo-facts spread in the health and beauty industry
Bibliographic record
Abstract
Purpose Diffusion of fake news and pseudo-facts is becoming increasingly fast-paced and widespread, making it more difficult for the general public to separate reliable information from misleading content. The purpose of this article is to provide a more advanced understanding of the underlying processes that contribute to the spread of health- and beauty-related rumors and of the mechanisms that can mitigate the risks associated with the diffusion of fake news. Design/methodology/approach By adopting denialism as a conceptual lens, this article introduces a framework that aims to explain the mechanisms through which fake news and pseudo-facts propagate within the health and beauty industry. Three exemplary case studies situated within the context of the health and beauty industry reveal the persuasiveness of these principles and shed light on the diffusion of false and misleading information. Findings The following seven denialistic marketing tactics that contribute to diffusion of fake news can be identified: (1) promoting a socially accepted image; (2) associating brands with a healthy lifestyle; (3) use of experts; (4) working with celebrity influencers; (5) selectively using and omitting facts; (6) sponsoring research and pseudo-science; and (7)exploiting regulatory loopholes. Through a better understanding of how fake news spreads, brand managers can simultaneously improve the optics that surround their firms, promote sales organically and reinforce consumers’ trust toward the brand. Originality/value Within the wider context of the health and beauty industry, this article sets to explore the mechanisms through which fake news and pseudo-facts propagate and influence brands and consumers. The article offers several contributions not only to the emergent literature on fake news but also to the wider marketing and consumer behavior literature.
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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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".