Abstract: In recent years widespread fake news has given rise to several social and political problems. Most of the knowledge today is acquired from digital sources. In Digital media it's difficult to assign accountability to the opinion thanks to which the info received can't be authenticated. Since the extent of ecological and societal issues, machine learning is especially relevant within the perspective of fake messages in Social Media. Anyone can make a message viral which may be a fake or real one. The goal is to understand a mechanism that's automatic, robust, reliable and efficient, despite various challenges which may help for the efforts to progress. In this i present the review on the state-of-the-art of faux news detection mechanisms on social media. After we discuss the background of the issues that are surrounding fake news and therefore the impacts it's on the users. We further discuss on different approaches presented in categories like the content-based, social context-based and hybrid-based methods. We conclude the paper with four keys of open research challenges which will guide the longer term research.
Keywords: Fake news detection, Sentence matching, Natural language processing, deep learning, and Word embedding, tf-idf, Sentiment, Machine Learning, Convolution Neural Networks, NLP, and Sentence Classification.
Title: A SURVEY ON FAKE NEWS DETECTION USING MACHINE LEARNING
Author: P.Anjeni Venkata Devi, Brahma Naidu
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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