Comparative Text Analysis on a Classification Task of Political Fake Statement Detection




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Automatic fake news detection is a very challenging problem especially in a fraud/deception detection and it has significant real-world political and social impact. During the 2016 US Presidential Election, the world saw many such cases. Thus, it is essential to address this socially relevant phenomenon. However, statistical approaches to combating fake news have been dramatically limited by the lack of a publicly available labeled dataset. Especially one with political news headline and their labels. Until now most of the research has been done on news articles or headline-article pair. But in this research, we emphasize only on political news headlines/statements spoken by political candidates or Facebook posts. This thesis explores different attempts on fake news detection task using a wide variety of natural language processing techniques. These techniques include extracting linguistic features from the statement, considering their predictive power by conducting feature engineering and topic modeling and determining the reputation of a speaker by his/her credit score, topic-speaker analysis, and word vectors. Using different classifiers, the overall approach is discussed. At the end, an attempt at stance detection is also discussed.



Fake news, Natural language processing (Computer science), Machine learning, Political science