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How to fight fake news with blockchain
3 min read

How to fight fake news with blockchain

Solving fake-news problem on a scale
How to fight fake news with blockchain

2016 was the year when the fake news became more influential than the real news for a short period. Buzzfeed news research shows that the fake news views even surpassed the mainstream news on the US presidential election day.

Nowadays, Β the fake news tactic becomes cheaper as well.

As Machine Learning technologies continue to evolve, the quality of the computer-generated or modified audio, video, and text is improving. The latest research demonstrated that "current models can generate samples of news article which human evaluators have difficulty distinguishing from articles written by humans".

So how can we fight the fake news lavish growth?

Anti-fake-news research

Multiple teams are working on the new technologies to catch the fake videos: DARPA has funded the project that identifies manipulated videos and images, Facebook has started a deep-fake detection challenge, NYTimes is addressing the problem with News Provenance Project and multiple other companies rating the news sources and articles.

Let's have a look at some of the solutions.

Deep-fake detection challenge

Facebook organized an open Deep-fake detection challenge to develop cutting-edge detection models. Facebook has even published a dataset of deep-fake videos for researchers.

The Factual

Another approach to fight the fake news is used by The Factual. The company provides a newsletter with credible stories as well as a browser plugin rating the content. The plugin provides The site quality, author expertise, sources quality, analyses the tone, political leaning as well as suggesting better articles covering the same topic.

Finally, the NYTimes launched a News Provenance Project group that explores the use of blockchain to fight the fake photos and media.

News Provenance Project

The project group tries to solve the problem by storing the photo metadata information, including original publisher, caption, photo credits, date, and location.

After the project launch, anyone will be able to verify the original source and the context of the original photo posted, as well as to read an original article and other opinions related to that photo.

These initiatives are addressing the fake news problems from different perspectives, but none is focusing on building a systemic self-regulated solution for all kinds of fake news.

What if ...

... we solve the fake news problem by creating a self-regulating ecosystem of publishers, content creators, and fact-checkers. The system that will provide

  1. the digital signatures for content creators and
  2. the incentives for fact-checking communities.

Let's dive into how this can be achieved.

Digital signatures for content creators

We can create a system that securely issues and stores the digital signatures for publishers and content creators with public keys and identities stored using the blockchain technology.

  1. The content creators and publishers use a smart-contract to issue a digital signature.
  2. The content creators and publishers can sign any content, including photos and articles, and store the signatures in the blockchain network.
  3. Anyone can verify if the content is original and an author, as well as a publisher, are trustable.

Fact-checking and author/publisher ratings

We can build a self-regulated rating ecosystem on top of the blockchain-verified signatures discussed above.

  1. Any signature stored in the blockchain will have a rating associated with it.
  2. Bounty hunters will search for unverified or modified data among the news for a reward. Bounty hunters will perform the fact-checking function of the ecosystem. Upon finding the fake news, a fact-checker will submit his findings and proof to the committee using the smart-contract.
  3. The committee will review the findings and will vote on-chain.
  4. If the committee decides the news article as fake, both the publisher and author will be automatically punished with the lower ratings or signature suspension.

Open problems

The solution still has gaps:

How can we choose the committee that will be trustable, without colluding and with reasonably low possibility to implement a 51% attack?

How to perform a KYC and verify the identity of the author or publisher?

Please, leave a comment or contact me if you have feedback, I've missed out on some important project or you have a solution to one of the open problems.

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