Misinformation About Climate Change and Related Environmental Events on Social Media: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Climate change and related environmental events represent major global challenges and are often accompanied by the spread of misinformation on social media. According to previous reviews, the dissemination of this misinformation on various social media platforms requires deeper exploration. Moreover, the findings reported applied mainly to the context of the United States, limiting the possibility of extending the results to other settings. OBJECTIVE: This study aims to assess the current state of knowledge about misinformation concerning climate change and related environmental events that are circulating on social media. More specifically, we will explore past and current themes, actors, and sources, and the dissemination of this misinformation within the Canadian context. METHODS: This scoping review protocol follows the methodological approach developed by Arksey and O'Malley and advanced by Levac, complemented by the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist and the best practice guidance for the development of scoping review protocols. Following the identification of the research questions and assisted by a specialized librarian, we developed search strategies for selected bibliographic databases (MEDLINE, Embase, Web of Science, and GreenFILE) and for gray literature (Google and pertinent databases) searches. Bibliographic and gray literature will be searched to identify relevant publications. In total, 2 members of our team will use the review software Covidence (Veritas Health Innovation) to independently select publications to include in the review. Publications specifically addressing our research questions, peer-reviewed, evidence-based, and published from January 1, 2000, in the full-text version in English or French will be included. Data will be extracted from the included publications to chart, among other items, the years of publication, geographic areas, themes, actors, and sources of the climate change-related misinformation and conclusions reported. Our team will then synthesize the extracted data to articulate the current state of knowledge relating to our research inquiries. RESULTS: The research questions were identified in January 2024. The search strategies were developed from January to March 2024 for MEDLINE, Embase, and Web of Science and in July 2024 for GreenFILE and gray literature. MEDLINE, Embase, and Web of Science searches were launched on March 26, 2024. The first of 2 rounds of selection of publications identified through these databases was achieved in April 2024. CONCLUSIONS: This protocol will enable us to identify the evolution of themes, actors, and sources of misinformation regarding climate change and related environmental events on social media, including the latest platforms, and to potentially identify a context particular to Canada. As misinformation is known to undermine actions and public support in the fight against climate change, we intend to facilitate the targeting of efforts to combat misinformation related to climate change in an up-to-date and contextualized manner. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59345.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".