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Identifier 000425812
Title Check-it: Real-time detection of fake news
Alternative Title Check-it: Ανίχνευση ψευδών ειδήσεων σε πραγματικό χρόνο
Author Κορνιλάκης, Αλέξανδρος Ι.
Thesis advisor Μαρκάτος, Ευάγγελος
Reviewer Πρατικάκης, Πολύβιος
Παπαδοπούλη, Μαρία
Abstract Over the past few years, we have been witnessing the rise of misinformation on the Internet. People fall victims of fake news continuously and contribute to their propagation knowingly or inadvertently. The use of propaganda is indeed ancient, but never before has there been the technology to so effectively disseminate it. The social media engagement that has swept our lives over the past decade practically exploded the proliferation of misinformation, including the associated distribution of fake news. The ‘pizzagate shooting’ incident and the Cambridge Analytica scandal indicate that we should not take this rise of misinformation lightly. Many recent efforts seek to reduce the damage caused by fake news by identifying them automatically with artificial intelligence techniques, using signals from domain flag-lists, online social networks, etc. In this thesis, we present Check-It, a system that combines a variety of signals into a pipeline for fake news identification. Such signals include the reputation of the person (account) posting the news, the reputation of the website where the news is hosted, the linguistic features that characterize a fake news article as well as the article’s content per se. Using a deep learning approach, we combine all these features towards providing a rating that is timely and accurate. Check-It is developed as a web browser plugin with the objective of efficient and timely fake news detection while respecting user privacy. The requirements we considered when designing Check-It is GDPR compliant, highly confident identification, low response time and lightweight computation. To implement our plugin, we have used pure JavaScript frameworks, like Minhash.js and TensorFlow.js. In this thesis, we present the design, implementation, and performance evaluation of Check-It. Experimental results show that it outperforms state-of-the-art methods on commonly used datasets while achieving an accuracy of 93%. Furthermore, we provide some directions that can guide future versions of Check-It.
Language English
Subject Misinformation
Natural language processing
Επεξεργασία φυσικής γλώσσας
Ψευδείς ειδήσεις
Issue date 2019-11-22
Collection   Faculty/Department--Faculty of Sciences and Engineering--Department of Computer Science--Post-graduate theses
  Type of Work--Post-graduate theses
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