Abstract
In this digital era, the dissemination of news is increasingly done online among the exploding population of Internet users. Due to the low cost involved and vanishing journalistic integrity, the spreading of unverified news, also called “fake news,” has become commonplace, often with long-lasting consequences to society. In these times of “digital deceit,” identifying fake news has become very important. This paper proposes a two-step approach using linguistic techniques in a deep learning framework to tackle the problem of fake news. Fake news may comprise fabricated news stories as well as “click baits.” Our approach involves identifying both these fake categories through a cascaded approach employing stance detection and entailment classification by building a complex model involving multiple deep neural networks. We use Term Frequency (TF), Cosine Similarity, and Word2Vec as features at different stages in the model. The datasets from Fake News Challenge 1 (FNC 1) and the Sentences Involving Compositional Logic (SICK) are used for stance detection and entailment classification, respectively. The focus of this work is, given an article, not to establish its credibility by identifying whether the article is counterfeit or not but to identify and eliminate the spread of fake news articles with respect to any particular spurious source.
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All authors discussed the contents of the manuscript and contributed to its preparation. While E. A. W and K. M designed the model and implemented the system, H. M and S. N helped with the manuscript. S. A and N. K. A provided critical feedback and helped shape the research, analysis, and final version of the manuscript.
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Williams, E.A., Karthik, M., Shahina, A., Murali, H., Safiyyah, N., Nayeemulla Khan, A. (2023). Snooping for Fake News: A Cascaded Approach Using Stance Detection and Entailment Classification. In: Venkataraman, N., Wang, L., Fernando, X., Zobaa, A.F. (eds) Big Data and Cloud Computing. ICBCC 2022. Lecture Notes in Electrical Engineering, vol 1021. Springer, Singapore. https://doi.org/10.1007/978-981-99-1051-9_11
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DOI: https://doi.org/10.1007/978-981-99-1051-9_11
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