Abstract
People can quickly obtain and publish the news through many platforms, i.e. social media, blogs, and websites, among others. Everything that is available on these platforms in not credible and it became imperative to check the credibility of articles before it proves to be detrimental for the society. Multiple initiatives have been taken up by platforms like Twitter and Facebook to check the credibility of news on their platforms. Several researches have been undertaken utilizing machine learning (ML) and deep learning (DL) methodologies to address the problem of determining the reliability of news. Traditional media solely employed textual content to spread information. However, with the introduction of Web 2.0, fake images have become more readily circulated. The news piece, along with the graphic statistics, lends credibility to the material. The picture data is occasionally supplemented with the news pieces. For this research, the prime focus is DL-based solutions for text-based fake news detection. This research discusses about various techniques to automated detection of fake news. The paper gives a comparative analysis of various techniques that have been successful in this domain. Various datasets that have been used frequently are also highlighted. Despite various researches have been conducted for tackling fake news, these approaches still lack in some areas like multilingual fake news, early detection and so on.
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Akter, Y., Arora, B. (2023). Deep Learning Techniques Used for Fake News Detection: A Review and Analysis. In: Singh, Y., Singh, P.K., Kolekar, M.H., Kar, A.K., Gonçalves, P.J.S. (eds) Proceedings of International Conference on Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 1001. Springer, Singapore. https://doi.org/10.1007/978-981-19-9876-8_11
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