Every day we receive a lot of information: news from Telegram channels, messages from relatives from other countries, posts on social networks. It's hard to tell which is true. What skill do we all need to master in one way or another?
Media literacy is, according to one definition, the ability to receive, analyze, evaluate and communicate messages in various forms.
A media literate person freely uses different types of media - from the Internet to television - and understands how they work, critically evaluates messages from different sources and can independently transmit these messages to other people. Media literacy also includes understanding the consequences of what we do online.
Media literacy helps you avoid misinformation. This is especially important now, when we are surrounded by a huge number of sources and fast and accessible information from them. News readers don't have as many ways to check information as a professional journalist. There is too much information around to look for the primary source of everything that we read daily. However, viral information cannot be ignored, and the best way to counter it is to still find the truth/original source/evaluate the information for virality.
Analytical tools for identifying disinformation have recently been developed quite widely. There are different models with different architectures: Fakebox, FakeDetector, TriFN and others. There are organizations, including international ones, that have been engaged in manual search and exposure of fakes for a long time: the International Fact-Checking Network, the Snopes website, etc.
The task of evaluating viral information is solved by classifiers trained using machine or deep learning algorithms. In the general case, they receive as input the text of the article with all metadata (title, author, heading, links to sources, publication date) and display an assessment of how this article is similar to fake or real news.
There are also more complex cases when we have colossal volumes of constantly generated, outdated and updated data at the same time. Most of the information found on the web is presented in textual form in natural language. This complicates its processing and requires the involvement of artificial intelligence, the development of new effective software systems for extracting facts from unstructured arrays of textual information, classification and clustering of information, aimed at both analyzing the messages themselves in the network and identifying the sources of disseminated information.
One of such systems to increase the accuracy of the checked content was created by the Exorde system. 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 as a knowledge network can use the unstructured content of the Internet and perform a first-of-its-kind analysis of the virality of information circulating throughout the network.
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 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. AI will be able to recognize objects in the text, extract sentences, facts, compare them with each other and perform similarity checks.
Written by Bimevox
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