Technique-based inoculation and accuracy prompts must be combined to increase truth discernment online
Bibliographic record
Abstract
Misinformation remains a serious problem and continues to be a major focus of intervention efforts. Psychological inoculation - a popular intervention approach wherein people are taught to identify manipulation techniques - is being adopted at scale around the globe by technology companies in an effort to combat misinformation. Yet the efficacy of this approach for increasing belief accuracy remains unclear, as prior work has largely focused on technique identification - rather than accuracy judgments - using synthetic materials that do not contain claims of truth or falsity. To address this issue, we conducted 5 studies with 7,286 online participants using a set of news headlines based on real-world false and true content in which we systematically varied the presence or absence of emotional manipulation. Although an emotional manipulation inoculation video did help participants identify emotional manipulation (replicating past work), there was no carry-over effect to improving participants’ ability to tell truth from falsehood (i.e. no effect on truth discernment). Encouragingly, however, when the emotional inoculation was paired with an accuracy prompt - i.e., an intervention intended to draw people’s attention to the concept of accuracy when they are receiving the inoculation intervention - the combined intervention did successfully improve truth discernment by increasing belief in true content. These results generate new insights regarding inoculation, and provide evidence for a key synergy between two popular psychological interventions against misinformation.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".