Trust-based Recommender System for Fake News Mitigation
Bibliographic record
Abstract
The ubiquity of fake news has been a serious problem on the Internet. Recommender systems, in particular, contribute to this issue by creating echo chambers of misinformation. In light of these observations, we address the issue of fake news mitigation through the lens of recommender systems. This paper introduces a novel adaptation of the collaborative filtering algorithm that models untrustworthy online users in order to remove them from the candidate user’s neighborhood. The proposed approach, FAke News Aware Recommender system (FANAR), is an alteration of the collaborative filtering strategy that considerably prevents the propagation of fake news by avoiding untrustworthy neighbors. Furthermore, we create FNEWR, a dataset for the Fake News Recommendation system, to fulfill our goal. Our experiments reveal that FANAR surpasses the current leading news recommendation techniques in its ability to suggest personalized news and mitigate the spread of false information.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".