The role of social media in communication about food risks
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the views of journalists, food regulators and the food industry representatives on the impact of social media on communication about food risk. The authors identify how journalists/media actors use social media in identifying and creating news stories arguing that food regulators need to maintain a social media presence to ensure that accurate information about food safety is disseminated via social media. Design/methodology/approach Data were collected through 105 semi-structured interviews. Findings While food regulators and representatives of the food industry identify advantages of social media including two-way communication and speed of transmission of information, they maintain concerns about information provided via social media fearing the potential for loss of control of the information and sensationalism. There is evidence, however, that media actors use social media to identify food stories, to find sources, gauge public opinion and to provide a human interest angle. Practical implications While there are commonalities between the three groups, concerns with social media reflect professional roles. Food regulators need to be aware of how media actors use social media and maintain a social media presence. Further, they need to monitor other sources to maintain consumer trust. Originality/value This paper adds to public debate through comparing the perspectives of the three groups of respondents each that have their own agendas which impact how they interact with and use social media.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".