Fake news is defined as a made-up story with an intention to deceive or to mislead. In this paper we present the solution to the task of fake news detection by using Deep Learning architectures. Gartner research  predicts that “By 2022, most people in mature economies will consume more false information than true information”. The exponential increase in production and distribution of inaccurate news presents an immediate need for automatically tagging and detecting such twisted news articles. However, automated detection of fake news is a hard task to accomplish as it requires the model to understand nuances in natural language. Moreover, majority of the existing fake news detection models treat the problem at hand as a binary classification task, which limits model’s ability to understand how related or unrelated the reported news is when compared to the real news. To address these gaps, we present neural network architecture to accurately predict the stance between a given pair of headline and article body. Our model outperforms existing model architectures by 2.5% and we are able to achieve an accuracy of 94.21% on test data.
Thota, Aswini; Tilak, Priyanka; Ahluwalia, Simrat; and Lohia, Nibrat
"Fake News Detection: A Deep Learning Approach,"
SMU Data Science Review: Vol. 1:
3, Article 10.
Available at: https://scholar.smu.edu/datasciencereview/vol1/iss3/10
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