Limited effectiveness of psychological inoculation against misinformation in a social media feed
Bibliographic record
Abstract
Psychological inoculation is a promising and potentially scalable approach to counter misinformation. The goal of inoculation is to teach people to recognize manipulation techniques, such as emotional language, commonly found in misinformation online. While there is substantial evidence that inoculation increases technique recognition when directly assessed, it is not clear if this effect transfers to spontaneous detection of techniques and disengagement with the associated content in real-life contexts. In particular, emotional appeals are abundant on social media and known drivers of attention and engagement. Therefore, we examined the effects of emotional language and emotional manipulation inoculation on attention and engagement in a simulated social media feed environment. Through five preregistered studies, we found that inoculation only decreased engagement with emotionally presented content when we solely presented synthetic content relevant to the task of identifying emotional manipulation. Any addition of real tweets or even synthetic tweets containing other manipulation techniques (e.g. ad hominem attacks) into the feed appeared to nullify the effect. Our results highlight the importance of assessing misinformation interventions in ecologically valid contexts to estimate real-world effects.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".