Personal Network Composition and Cognitive Reflection Predict Susceptibility to Different Types of Misinformation
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
Abstract Despite a rapid increase in research on the underpinnings of misinformation susceptibility, scholars still disagree about the relative impacts of social context and individual cognitive factors. We argue that cognitive reflection and identity-based network homogeneity may have unique influences on different types of misinformation. Specifically, identity-based network homogeneity predicts bias that is related to any type of identity-based information (i.e., political rumors), and cognitive reflection is more tailored toward truth discernment (i.e., fake news headlines). We conducted our study using an online sample (N = 214) split evenly between Democrats and Republicans and collected data on personal network composition, cognitive reflection, as well as susceptibility, sentiments, and sharing behavior in relation to political rumors and misinformation, respectively. Results demonstrate that where network homogeneity predicts belief and sharing in both political rumors and fake news headlines, cognitive reflection only predicts belief and sharing of fake news headlines. Social vs. cognitive factors for predicting different types of misinformation are discussed.
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 | 0.000 | 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 | 0.000 |
| 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