# Blockchain-Based Platform to Fight Disinformation Using Crowd Wisdom and Artificial Intelligence
###### Abstract  
Disinformation and fake news are used by multiple actors to manipulate and influence the public with the purpose of gaining a series of advantages. This paper describes a promising solution to the increased spread of disinformation on the Internet. Our approach leverages blockchain technology combined with both crowd intelligence and federated artificial intelligence to develop efficient capabilities that address the disinformation phenomenon. The blockchain-based architecture of the platform creates a decentralized ecosystem that ensures transparency and trust, enabling the users to make correctly informed decisions in the face of disinformation. The key differentiating factor of the platform is the incorporation of both crowd and artificial intelligence in a system that can identify and respond to disinformation quickly and efficiently. The presented architecture can be used to build reactive and proactive platforms to effectively challenge disinformation. blockchain; fighting disinformation; crowd wisdom; artificial intelligence; governance protocol; cryptocurrency tokens +
Footnote †: journal: Medical  
confusion and huge duplication of work, which is not sustainable in the long run. Thus, even though the intention of fact-checkers is admirable it does not make them a viable solution to solve the issue of disinformation. No technology can fully solve the challenges of establishing trust between people, nor eliminate the underlying motivations for profit and/or political gain that drive disinformation in the first place. This is why, to solve the problem of disinformation and fake news, society must fight back using various tools, ranging from technological tools (like blockchain [3] and AI [4]) to educational tools. This paper describes a decentralized anti-disinformation platform for fact checking and trust assessment based on Blockchain, Crowd Wisdom and AI technologies. At present, trust in mainstream media is lower than ever due to ever increasing disinformation levels and the amount of information that cannot be reliably checked. In the current media landscape, publications are driven by click-based ad revenue. To increase the number of views (and therefore to increase their income), publications tend to take journalistic shortcuts (like not thoroughly verifying the facts or presenting incomplete information just to be the first to announce some news). In this landscape, even reputable publications tend to favor engagement over clarity. This in turn impacts the readers' ability to distinguish the truth from disinformation and fake news. A survey [5] regarding trust in the media (television, radio, written press, internet and social networks) was conducted in 2019 in all 28 EU countries. It was found that 41% of Europeans had medium trust in the media, 40% of the EU citizens had low or no trust in the media at all and only 19% of adults had high trust in the news media. So, the population in general is aware about the media quality and the spread of disinformation and fake news. That was in 2019 and since then, the sensitive geopolitical context further increased the spread of disinformation and fake news. Because, in recent years, disinformation and misinformation spread increased to levels never seen before, the lack of trust in the media also increased in the general population. This affects all publishers, not only the ones responsible for spreading fake news. The lack of trust that consumes our society can be fixed by revealing, in a trusted way, what is true and what is not true. The platform described in this paper comes to address these issues, to expose disinformation and misinformation spread online by dishonest publishers and to finally restore trust to reputable publishers. Currently, there are not enough platforms that can provide valuable and scalable fact-checking. This is in part because their operation is not sustainable for the long term, due to the dependency on paid human interaction.