Media Source Characteristics Regarding Food Fraud Misinformation According to the Health Information National Trends Survey (HINTS) in China: Comparative Study
Bibliographic record
Abstract
BACKGROUND: Ongoing rumors and fake news regarding food fraud, adulteration, and contamination are highly visible. Health risk information circulating through media and interpersonal communication channels has made health crisis an important research agenda. OBJECTIVE: This study explored the issue of food fraud and the effect of misinformation. Further, it assessed whether and how these issues have provided evidence-based interventions for food handlers and regulators to mitigate fraud misinformation. METHODS: The Health Information National Trends Survey (HINTS) was adopted for a collaborative study in China, after which a cross-sectional survey with door-to-door interviews was performed. Participants from Beijing and Hefei were selected using multistage sampling of adults in May 2017. Based on 4 government surveillance reports on food rumors and safety incidents, a descriptive analysis, correlation analysis, and analysis of variance were performed on the data. RESULTS: A total of 3090 results were gathered and analyzed. Among the respondents, 83.6% (2584/3090) heard at least one food rumor. Learning about food fraud was correlated with interpersonal connections (eg, doctors or health specialists) for accessing food health information. Overall, Chinese citizens with a higher level of interpersonal connection were more likely to be concerned about food incidents with a statistical difference (P<.001). Interpersonal connection was the most frequent communication source (698/1253, 55.7%), followed by traditional media (325/1253, 25.9%) and internet portals (144/1253, 11.5%). There was a significant relationship between media use and media category in Beijing (P<.001) and Hefei (P<.001). Overall, responses to food fraud and incident risks were lower in Beijing than in Hefei (P=.006). The respondents in Beijing were confronted more frequently by food rumors (range 346-1253) than those in Hefei (range 155-946). The urban dwellers in Beijing and their rural counterparts in Hefei also differed in terms of perceiving different levels of food risks from different media sources. The food rumor narratives that examined the conspiracy belief showed that social media played more important roles in influencing attitudes against misinformation for users in Hefei than in Beijing. CONCLUSIONS: This study shows that consumers have to be on guard against not only fake food, but also spreading fake information and rumors, as well as conspiracy beliefs involving fake food. This study focused on characterizing media sources, types of food fraud misinformation, and risk perceptions of food safety, which mix urgency and suspicion, and attempted to provide evidence-based interventions for risk management guidance, with the hypothesis of significant correlations between media types and sources, and consumer exposure and perception levels of food rumors and risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".