Every day we are faced with a flood of information. It can be contradictory, frightening, absurd. And, even if at first glance it seems that this in no way affects ordinary life, then this is far from the case.

Fakes, as a product of media resources, have become the subject of wide discussion in the past few years, especially during Brexit, military clashes and the US presidential election. Of course, the use of false information is not new: disinformation as a method of influencing the enemy was advised by the Chinese commander Sun Tzu. The ubiquity of the Internet and the growing popularity of social networks led to a partial transition of communication, including political, to the online environment, which stimulated the spread of fakes.

Fake messages can be created and disseminated for various purposes: entertaining an audience, increasing traffic, achieving political or competitive advantages, discriminating against certain groups of people, drawing attention to an individual or company, fraudulently taking money or property of users, damaging information stored on computers users.

At the same time, the audience is often unable to separate real news from fake news due to the low level of media literacy. Thus, according to the National Financial Research Agency (NAFI), the media literacy index of Russians, i.e. checking the reliability of news, searching for news in different sources, understanding that news may not be fully covered, is 65% out of 100 possible. Moreover, 51% of respondents do not compare information from different media sources, which means they tend to trust one point of view, which may turn out to be fake. Even after a while, about half of the audience remembers fake messages and continues to believe them.

Given the scale of the spread of false information, the need for quick and effective solutions in the fight against fakes is more urgent than ever. Modern text classification technologies based on machine learning make it possible to speed up and partially automate the process of filtering out disinformation. But, in order to avoid bias, you should not trust the task of recognizing false information to any one specialized center or verification algorithm. Even the most complex deep learning-based classification models are optimal only at the time of creation, and their accuracy decreases over time as new fakes appear and the style of writing them changes. The task of decentralization can be solved, for example, with the help of distributed content verification architectures based on blockchain.

Exorde has become one of these ecosystems. Exorde is built around a core platform that provides unbiased credibility scores for information (and virality-related analytics) based on the community, AI modules, and a token-based economy.

This platform is decentralized, open and transparent. This is where participants will work together to index the entire network, extract its unstructured information, relationships, similarities, trends, and any type of pattern in information circulating throughout the internet, regardless of platform or media.

Exorde is run by its DAO (Decentralized Autonomous Organization) and uses community votes and polls. Management will be decentralized among all members of the community. Collectively, they will be able to change the internal rules and parameters of the systems (rewards, limits, delays, scheduling, etc.) and will have a built-in reputation system. These mechanisms are designed to continually align the interests of the community and its governance for the benefit of Exorde.

The Exorde Work Systems are the core component and will serve as the base layer for the entire ecosystem. Exorde is based on four main parameters:

Ethereum is an open source platform that allows developers to create and deploy decentralized applications on it.

Skale SKALE is an elastic sidechain network connected to the Ethereum chain, acting as an execution layer. It is a scalable and elastic environment that provides high transaction throughput.

Filecoin is a decentralized data storage system that intends to replace the expensive and inefficient servers of Google and Amazon.

NLP is an artificial intelligence module that will work with unstructured text.

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