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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".