Combating Fake News on Social Media: A Framework, Review, and Future Opportunities
Bibliographic record
Abstract
Social media platforms facilitate the sharing of a vast magnitude of information in split seconds among users. However, some false information, generally referred to as “fake news”, is also widely spread. This can have major negative impacts on individuals and societies. Unfortunately, people are often not able to correctly identify fake news from authentic news. Therefore, there is an urgent need to find effective mechanisms to fight fake news on social media. To this end, this paper adapts the Straub Model of Security Action Cycle to the context of combating fake news on social media. It uses the adapted framework to classify the vast literature on fake news into action cycle phases (i.e., deterrence, prevention, detection, and mitigation/remedy). Based on a systematic and inter-disciplinary review of the relevant literature, we analyze the status and the challenges in each stage of combating fake news, followed by introducing future research directions. These efforts allow the development of a holistic view of the research frontier on fighting fake news online. We conclude that this is a multidisciplinary issue, and as such, a collaborative effort from different fields is needed to effectively address this problem.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".