Misinformation effects in an online sample: results of an experimental study with a five day retention interval
Bibliographic record
Abstract
Traditional face-to-face laboratory studies have contributed greatly to our understanding of how misinformation effects develop. However, an area of emerging concern that has been relatively under-researched is the impact of misinformation following exposure to traumatic events that are viewed online. Here we describe a novel method for investigating misinformation effects in an online context. Participants ( N = 99) completed the study online. They first watched a 10-min video of a fictional school shooting. Between 5 and 10 days later, they were randomly assigned to receive misinformation or no misinformation about the video before completing a recognition test. Misinformed participants were less accurate at discriminating between misinformation and true statements than control participants. This effect was most strongly supported by ROC analyses (Cohen’s d = 0.59, BF10 = 8.34). Misinformation effects can be established in an online experiment using candid violent viral-style video stimuli.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".