Blockchain-based Framework for Reducing Fake or Vicious News Spread on Social Media/Messaging Platforms
Bibliographic record
Abstract
With social media becoming the most frequently used mode of modern-day communications, the propagation of fake or vicious news through such modes of communication has emerged as a serious problem. The scope of the problem of fake or vicious news may range from rumour-mongering, with intent to defame someone, to manufacturing false opinions/trends impacting elections and stock exchanges to much more alarming and mala fide repercussions of inciting violence by bad actors, especially in sensitive law-and-order situations. Therefore, curbing fake or vicious news and identifying the source of such news to ensure strict accountability is the need of the hour. Researchers have been working in the area of using text analysis, labelling, artificial intelligence, and machine learning techniques for detecting fake news, but identifying the source or originator of such news for accountability is still a big challenge for which no concrete approach exists as of today. Also, there is another common problematic trend on social media whereby targeted vicious content goes viral to mobilize or instigate people with malicious intent to destabilize normalcy in society. In the proposed solution, we treat both problems of fake news and vicious news together. We propose a blockchain and keyed watermarking-based framework for social media/messaging platforms that will allow the integrity of the posted content as well as ensure accountability on the owner/user of the post. Intrinsic properties of blockchain-like transparency and immutability are advantageous for curbing fake or vicious news. After identification of fake or vicious news, its spread will be immediately curbed through backtracking as well as forward tracking. Also, observing transactions on the blockchain, the density and rate of forwarding of a particular original message going beyond a threshold can easily be checked, which could be identified as a possible malicious attempt to spread objectionable content. If the content is deemed dangerous or inappropriate, its spread will be curbed immediately. The use of the Raft consensus algorithm and bloXroute servers is proposed to enhance throughput and network scalability, respectively. Thus, the framework offers a proactive as well as reactive, practically feasible, and effective solution for curtailment of fake or vicious news on social media/messaging platforms. The proposed work is a framework for solving fake or vicious news spread problems on social media; the complete design specifications are beyond scope of the current work and will be addressed in the future.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".