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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".