Systematic Review of Misinformation in Social and Online Media for the Development of an Analytical Framework for Agri-Food Sector
Bibliographic record
Abstract
The ubiquity of social and online media networks, the credulousness of online communities, coupled with limited accountability pose a risk of mis-, dis-, mal-, information (mis-dis-mal-information)—the intentional or unintentional spread of false, misleading and right information related to agri-food topics. However, agri-food mis-dis-malinformation in social media and online digital agricultural communities of practice (CoPs) remains underexplored. There is also a limited theoretical and conceptual foundation for understanding mis-dis-malinformation topics in the agri-food sectors. The study aims to review mis-dis-malinformation literature and offer a framework to help understand agri-food mis-dis-malinformation in social media and online CoPs. This paper performs a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The review shows that many disciplines, particularly communication, social media studies, computer science, health studies, political science and journalism, are increasingly engaging with mis-dis-malinformation research. This systematic research generates a framework based on six thematic categories for holistically understanding and assessing agri-food mis-dis-malinformation in social and online media communities. The framework includes mis-dis-malinformation characterization, source identification, diffusion mechanisms, stakeholder impacts, detection tactics, and mis-dis-malinformation curtailment and countermeasures. The paper contributes to advancing the emerging literature on ‘controversial topics’, ‘misinformation’, and ‘information integrity’ of the virtual agri-food advisory services. This is the first attempt to systematically analyze and incorporate experience from diverse fields of mis-dis-malinformation research that will inform future scholarly works in facilitating conversations and advisory efforts in the agri-food sector.
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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 | 0.051 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.029 | 0.024 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".