This dissertation was written as a part of the MSc in Data Science at the International Hellenic University. The study is based on fake news detection with machine learning concepts. Literature review on fake news was conducted in order to review the most significant theory concepts and realize the level of advancement regarding this topic by examining related work. A total number of 940 data points were extracted through a daily web scrapping procedure. The research part provides an experimental analysis with 5 well known classifiers and results are evaluated by appropriate metrics. Finally, the last part of the study is referring to the innovation of this study, the Ranking Model approach, which is capable of labeling new inputs as fake or real.
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