Conspiracy Beliefs, Misinformation, Social Media Platforms, and Protest Participation
Bibliographic record
Abstract
Protest has long been associated with left-wing actors and left-wing causes. However, right-wing actors also engage in protest. Are right-wing actors mobilized by the same factors as those actors on the left? This article uses cross-national survey data (i.e., US, UK, France, and Canada) gathered in February 2021 to assess the role of misinformation, conspiracy beliefs, and the use of different social media platforms in explaining participation in marches or demonstrations. We find that those who use Twitch or TikTok are twice as likely to participate in marches or demonstrations, compared to non-users, but the uses of these platforms are more highly related to participation in right-wing protests than left-wing protests. Exposure to misinformation on social media and beliefs in conspiracy theories also increase the likelihood of participating in protests. Our research makes several important contributions. First, we separate right-wing protest participation from left-wing protest participation, whereas existing scholarship tends to lump these together. Second, we offer new insights into the effects of conspiracy beliefs and misinformation on participation using cross-national data. Third, we examine the roles of emerging social media platforms such as Twitch and TikTok (as well as legacy platforms such as YouTube and Facebook) to better understand the differential roles that social media platforms play in protest participation.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".