# Blockchain-Based Platform to Fight Disinformation Using Crowd Wisdom and Artificial Intelligence
## 3 Related Work  
There is an increased scientific effort to develop new solutions based on blockchain that try to detect and fight against fake news. Many scientific papers that deal with different aspects of the fight against misinformation and disinformation were written. In [18], the authors propose a blockchain platform focused on the \"fact\", where the news posted on the platform by news publishers will be analyzed by an AI component. This platform is similar to a social media platform where the content is generated and consumed within it. Having the content posted and reposted inside the platform, allows to easily track the source and the propagation path of the news. Another solution is presented in [19], where the authors present an incentive-aware blockchain-based solution for internet of fake media things. The system architecture runs on a customized PoA (Proof-of-Authority) protocol. Here, news organizations must register to be able to publish news into the blockchain network, and the nodes, represented by news publishers, are also in charge of news validation. The authors of [20] introduce a blockchain solution that uses a deep learning hybrid model to detect fake news. Their approach uses a pretrained GloVe (Global Vectors for Word Representation) embedding matrix for word embeddings. These embeddings are used as input for deep learning models to classify the news. The news is directly published into their system by reporters, analyzed by analyzers and validated by validators. There are three types of analyzers: deep learning, verified journalists and individuals. In [21], the authors propose a combination of blockchain and machine learning techniques to detect fake news. They use NLP (Natural Language Processing) and reinforcement learning techniques and a customized blockchain consensus algorithm based on PoA protocol. Another blockchain-based solution for fake news detection in social media is given in [22]. In this approach, the news is published into their system and random users are selected to act as validators. To select the users that are closer to the source of the news event, the system uses the BFS (Breadth First Search) algorithm to calculate the proximity of a user. The authors of [23] propose a system where multiple news sources and social media platforms use the same blockchain to track how users share the news on their timeline. A modified version of a posted news can be easily tracked, flagged and removed from the system. However, this system assumes that the original published news is true and only detects if a user modifies the content on his timeline. In another paper [24], the authors present a blockchain system featuring machine learning for mitigating the effects of fake news called Reliable News Sharing Platform. The news is published by publishers into the system. There are four entities involved in the system: reporters, machine learning analyzer, miners and readers. The system uses NLP and CNN (Convolutional Neural Networks) to analyze the news. The news articles are stored in the InterPlanetary File System (IPFS) and only a reference to IPFS id is stored on the blockchain. A news verification blockchain system based on Ethereum and IPFS is presented in [25]. In this system, the journalists use a smart contract to publish news on the blockchain, after which they are validated by validators. The news content is stored on IPFS and the IPFS id is stored on blockchain, similar to the approach presented in [24]. In [26], a private blockchain and watermarking based social media framework is proposed to control the fake news propagation. In this system, users must register and provide their identity. The model focuses on identifying the source of news and verifies the fake news based on user reports. Most solutions, which are trying to solve the problem of fake news, try to establish a news platform where publishers publish their news articles.