The Social Media Context Interferes with Truth Discernment
Bibliographic record
Abstract
There is widespread concern about fake news and other misinformation circulating on social media. In particular, many argue that the context of social media itself may make people particularly susceptible to the influence of false claims. Here, we test that claim by asking whether simply considering whether to share news on social media reduces people’s ability to identify truth versus falsehood. In a large online experiment (N=3,157 Americans quota-matched to the national distribution of age, gender, ethnicity, and geographic region) examining COVID-19 and political news, we find support for this possibility. Compared to a baseline where participants judged only the accuracy of each headline, we observed worse truth discernment when participants also indicated their sharing intentions. Conversely, sharing discernment was substantially higher when participants also rated accuracy, relative to a baseline where sharing intentions were elicited without rating accuracy. These results suggest people may be particularly vulnerable to believing false claims on social media due to fundamental features of these platforms – which is particularly concerning given that it is hard to imagine social media without sharing.
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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 | 0.010 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".