Who to Trust on Social Media: How Opinion Leaders and Seekers Avoid Disinformation and Echo Chambers
Bibliographic record
Abstract
As trust in news media and social media dwindles and fears of disinformation and echo chambers spread, individuals need to find ways to access and assess reliable and trustworthy information. Despite low levels of trust in social media, they are used for accessing political information and news. In this study, we examine the information verification practices of opinion leaders (who consume political information above average and share their opinions on social media above average) and of opinion seekers (who seek out political information from friends and family) to understand similarities and differences in their news media trust, fact-checking behaviors, and likeliness of being caught in echo chambers. Based on a survey of French Internet users ( N = 2,000) we find that not only opinion leaders, but also opinion seekers, have higher rates across all three of these dependent variables. We discuss the implications of findings for the development of opinion leadership theory as well as for social media platforms wishing to increase trust.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".