A misinformation newsstand is seen in midtown Manhattan aiming to educate news consumers about the dangers of disinformation, or fake news Photo by ANGELA WEISS / AFP

Blockchain can help filter the fake news dilemma

  • Blockchain-based solutions have the potential to change the way information is produced and disseminated while playing a major role to tackle disinformation over the longer term
  • Due to its traceability, transparency, and decentralization nature of the blockchain, the problem of fake news can be handled effectively

Gartner predicts that the majority of individuals in developed economies will consume more false than true information by 2022. While trust in mass media and established institutions is declining, the use of online social media to connect is rising sharply and it has become an important source for the distribution of digital deception. Researchers claim that although fake news detection could be a complicated process, the traceability of the data, the communications architecture, and the transactions, can be controlled.

That being said, blockchain and other Distributed Ledger Technologies (DLTs) are the rising technologies that can help to combat digital deception. These technologies enable privacy, security, and trust in a decentralized Peer-to-Peer (P2P) network without any central managing authority. In fact, as blockchain gains credibility, it is being piloted for uses never before considered. Groups, as varied as newsrooms, nonprofits, major corporations, and start-ups, are all eagerly pursuing the technology to create distributed, transparent networks for reliable media and digital information.

How does blockchain help?

The main issue about fake news is the rapid speed with which it disseminates. While false information has always existed, the internet makes it worse every year. The high speed of fake news sharing has the potential to directly affect public relations and have serious political and economic consequences that are sometimes difficult to predict.

For decades, manual fact-checking was the way to go, before technologies for fighting fake news, like automated source finding, or an anti-plagiarism system came about. Then came projects and studies on using various machine learning techniques to identify inaccurate information. These projects are most often based on stylistic analysis of texts and a model that has been trained on fake-news text examples. Nonetheless, there are also limitations here, such as the collection and markup of the database, as it is a very time-consuming process.

However, there are examples of successful projects, such as when Twitter acquired a British artificial intelligence-based startup to help it combat the amount of fake news being spread on its platform. While blockchain, a relatively new technology, won’t necessarily stop people from posting false information, it could, at minimum, foster a new sense of trust in what they see online by making it easier to track and verify. Such efforts could also encourage the public to exercise a healthier skepticism of online media overall.

According to a recent report from Gartner, Predicts 2020: Blockchain Technology, by 2023, up to 30% of world news and video content will be authenticated as real by blockchain ledgers, countering Deep Fake technology. Gartner also stated that The New York Times is one of the first major news publications to test blockchain to authenticate news photographs and video content. The newspaper’s Research and Development team and IBM have partnered on the News Provenance Project, which uses Hyperledger Fabric’s permissioned blockchain to store “contextual metadata.” That metadata includes when and where a photo or video was shot, who took it, and how and when it was edited and published.

That said, with the help of blockchain platforms, news sites can increase their transparency, and getting to the source of misinformation will become much easier and faster. Not only will this help another end-user verify the information, but it will also provide evidence of the metadata collected at each stage.