Understanding Users' Intention to Verify Content on Social Media Platforms.
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
<p>The spread of inaccurate or “fake” content over social media platforms (SMP) has become a major societal challenge with significant social and political repercussions. Studies in the IS field have examined the credibility of online information as a key construct. However, little attention is paid to the behavior of individuals when faced with questionable information. This study draws from the elaboration likelihood model and theory of attribution to develop a research model that explains the conditions under which people verify messages that they receive and view over SMP. We theorize that message quality and the relational proximity of the sender influence the likelihood of verifying content, and that incongruence of the content with prior beliefs of the receiver moderates the influence of message quality on the intention to verify. We test our model using the vignette method with four scenarios. The initial results and implications of these results are discussed.</p>
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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