Shifting attention to accuracy can reduce misinformation online
Bibliographic record
Abstract
Why do people share false and misleading news content on social media, and what can be done about it? In a first survey experiment (N=1,015), we demonstrate a disconnect between accuracy judgments and sharing intentions: Even though true headlines are rated as much more accurate than false headlines, headline veracity has little impact on sharing. Although this may seem to indicate that people share inaccurate content because, for example, they care more about furthering their political agenda than they care about truth, we propose an alternative attentional account: Most people do not want to spread misinformation, but the social media context focuses their attention on factors other than truth and accuracy. Indeed, when directly asked, most participants say it is important to only share news that is accurate. Accordingly, across four survey experiments (total N=3,485) and a digital field experiment on Twitter in which we messaged users who had previously shared news from websites known for publishing misleading content (N=5,379), we find that subtly inducing people to think about accuracy increases the quality of the news they subsequently share. These results, together with additional computational analyses, challenge the narrative that people no longer care about accuracy. Instead, the findings support our inattention-based account wherein people fail to implement their preference for accuracy due to attentional constraints – particularly on social media. Furthermore, our research provides evidence for scalable anti-misinformation interventions that are easily implementable by social media platforms.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".