Polluting the discourse: How misinformation impacts climate change advocacy
Bibliographic record
Abstract
While scientists, governments, and climate change advocates grapple with finding solutions for the devastating consequences of global warming, communicating this urgency to the public has proved to be an even larger challenge (Marshall, 2014). Communication barriers such as fuel-industry interference, the public’s lack of scientific literacy, and the public’s inability to comprehend the risk that climate change poses (Marshall, 2014), are hindering advocates’ efforts to make the necessary change to mitigate this existential threat. Furthermore, the increased use of social media to disseminate information has led to echo chambers and an environment in which misinformation spreads faster than credible information (Treen et al., 2020). Through in-depth interviews with senior climate change advocates and communications specialists, complemented by a content analysis of social media climate change discussions, this study examines these communications barriers and identifies potential solutions to creating impactful campaigns. Using a content analysis as a secondary research method, it. demonstrates that misinformation is more likely to be shared on Facebook than on Twitter and that while misleading climate change information receives the most shares from online users, fabricated misinformation actually has the highest reach. The results reveal that an audience-tailored approach that considers individual motivations and social identities, and focuses on building trust, can help advocates advance their organizations’ missions through effective communications strategies. Further research is recommended to conduct empirical testing of these strategies to provide quantitative evidence of their efficacy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 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; both teacher heads agree on what is shown here.
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".