To Share or Not to Share: Randomized Controlled Study of Misinformation Warning Labels on Social Media
Bibliographic record
Abstract
Abstract Can warning labels on social media posts reduce the spread of misinformation online? This paper presents the results of an empirical study using ModSimulator, an open-source mock social media research tool, to test the effectiveness of soft moderation interventions aimed at limiting misinformation spread and informing users about post accuracy. Specifically, the study used ModSimulator to create a social media interface that mimics the experience of using Facebook and tested two common soft moderation interventions – a footnote warning label and a blur filter – to examine how users (n = 1500) respond to misinformation labels attached to false claims about the Russia-Ukraine war. Results indicate that both types of interventions decreased engagement with posts featuring false claims in a Facebook-like simulated interface, with neither demonstrating a significantly stronger effect than the other. In addition, the study finds that belief in pro-Kremlin claims and trust in partisan sources increase the likelihood of engagement, while trust in fact-checking organizations and frequent commenting on Facebook lowers it. These findings underscore the importance of not solely relying on soft moderation interventions, as other factors impact users’ decisions to engage with misinformation on social media.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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 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".