Who to Trust on Social Media: How Opinion Leaders and Seekers Avoid Disinformation and Echo Chambers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it