Toxicity and Misinformation: Drivers of Social Media Discontinuation and Implications for Facilitating Agricultural Innovation in Ontario
Bibliographic record
Abstract
Social media platforms are increasingly pivotal for facilitating agricultural innovations, yet their effectiveness in Ontario’s agri-food sector is compromised by misinformation, toxicity, and user discontinuation. This study examines how agri-food actors (producers, advisors, policymakers, researchers, and industry representatives) engage with social media for advisory services, the drivers of platform abandonment, and preferred alternatives for receiving emerging technology information. Deploying an online survey via Qualtrics (*n* ≈ 40–60 purposively sampled stakeholders), the research assesses: (1) frequency and purposes (e.g., networking, outreach, crowdsourcing) of social media use across platforms (Facebook, X, LinkedIn, YouTube, etc.); (2) reasons for reducing or quitting platforms (e.g., misinformation prevalence, anti-social behavior, privacy concerns); and (3) shifts in communication channels post-discontinuation. Thematic analysis of open-ended responses contextualizes quantitative trends, particularly regarding misinformation encounters (e.g., false agri-tech claims) and experiences with harassment (e.g., identity-based attacks). Crucially, the study identifies how these factors impede technology diffusion and stakeholder trust. Preliminary insights suggest heterogeneity in platform preferences across demographics (age, professional role) and commodity sectors (livestock, crops), with implications for designing resilient, inclusive advisory systems. By mapping discontinuation drivers and channel migration patterns, this work will inform evidence-based strategies—including hybrid digital-in-person approaches and platform-specific content moderation protocols—to optimize agricultural innovation outreach in Ontario. Findings aim to strengthen policy frameworks and extension programs, ensuring timely, credible knowledge transfer amid evolving digital risks.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".