Tracing the Source of Fake News using a Scalable Blockchain Distributed Network
Bibliographic record
Abstract
In the news industry, as well as in social media, fake news detection and identification of news sources has become a central topic of discussion. In the era of digitization, anyone can easily generate or manipulate digital content and publish them on social media websites. On the one hand, these social networking platforms provide ample ease in modern-day communication but on the other hand, using such platforms has posed new challenges to real-world implementation like viral spreading of false/fake information with malicious intentions. In this paper, a naive blockchain and watermarking based social media framework is proposed to control the fake news propagation. We postulate a new blockchain model to mitigate existing challenges in this field. Moreover, the novel solution can help in reducing the spread of fake news by tracing the root or origin of the fake news on social media. Through our experimental results, we show that our blockchain-based solution is able to immediately stream data through a bloXroute server that can propagate data up to 100 times faster than conventional solutions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".