Concerns about misinformation on Instagram in five countries
Bibliographic record
Abstract
Use of Instagram has proliferated over recent years, as have concerns about misinformation. Yet, most misinformation research has focused on Facebook and Twitter. Based on a survey of more than 4000 Instagram users from the United States, the United Kingdom, France, Canada, and Germany, this study examines general predictors of concerns about misinformation on Instagram and predictors specific to the platform’s information environment. We highlight three potential conceptual accounts of misinformation concerns: fears of being misled due to (incidental) exposure to misinformation, exposure to politically cross-cutting content, and third-person effects. We find that seeing political content on Instagram (from one’s network or other sources) positively and significantly relates to concerns about misinformation, while the political heterogeneity of one’s network does not. Neither political interest nor ideology relate to concerns over misinformation on Instagram, but users’ perceived ability to identify misinformation does. These findings indicate that concerns about misinformation on Instagram are largely related to a third-person effect. We examine if findings replicate across all five countries. Concerns about misinformation are important to understand as they relate to increased vigilance and thus, reduced susceptibility to misinformation, to institutional trust, and to support for government interventions to combat misinformation.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".