An Implementation of Fake News Prevention by Blockchain and Entropy-based Incentive Mechanism
Bibliographic record
Abstract
Fake news is undoubtedly a significant threat to democratic countries nowadays because existing technologies can quickly and massively produce fake videos, articles, or social media messages based on the rapid development of artificial intelligence and deep learning. Therefore, human assistance is critical if current automatic fake new identification technologies desire to improve accuracy. Given this situation, prior research has proposed to add a quorum, a group of appraisers trusted by users to verify the authenticity of the information, to the fake news prevention systems. This paper proposes a stake-based incentive mechanism to diminish the negative effect of malicious behaviors on a quorum-based fake news prevention system. Moreover, we use Hyperledger Fabric, Schnorr signatures, and human appraisers to implement a practical prototype of a quorum-based fake news prevention system. Then we conduct necessary case analyses and experiments to realize how dishonest participants, crash failures, and scale impact our system. The outcomes of the case analyses and experiments show that our mechanisms are feasible and provide an analytical basis for developing fake news prevention systems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".