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Monster: Monitoring News For Trust Enhanced Recovering
Fake news have existed for many years. There are records that even point to Ancient Rome. Since then, our society has suffered the negative effects derived from fake news. They are spread quickly, specially nowadays, with the massive use of social networks. For this reason, it is of main interest to recover users' trust in the received information. Like this, detection and prevention are important to avoid the polarization in people's thoughts and other side negative effects of fake news. In the last years, both science and industry are making a huge effort to propose solutions to help reducing the number of fake news and their effect in our society. These efforts have been traditionally focused in the application of Machine Learning (ML) techniques. However, with the growing success of blockchain, there is a trend to apply this technology to ensure both integrity and traceability of the news content. In this work, we present Monster, a hybrid approach that combines both ML and blockchain in a single architecture for fake news detection.