Abstract
The modern bloom of social media has propelled a new pattern of information propagation termed push journalism, where a certain piece of news is shoved in the faces of as many people as possible with a sliver of hope that it will reach the people who need that information the most. This form of news reporting, especially via social media campaigns has boosted the access and fabrication of bogus reporting, or what is referred to as fake news. Fake news, in the form of clickbait, hoax, satire, propaganda, hyperpartisan, deepfakes, or simply unreliable news has the power of influencing its readers to a dangerous extent, predominantly causing political, socio-economic, or psychological harm. In this chapter, we analyze the meaning of fake news in the world of social media, the various forms it can take, what causes its spread, and what are the rudimentary signs of such fake news. We will walk through a comparative study of the state-of-the-art deep learning models to approach the tasks of identifying phony information, verifying the validity of various claims and facts, catching fake content, and so on. The exposition will especially elucidate the adversarial approaches in deep learning to detect counterfeit content that could come in any form like text, images, videos, or audio. In doing so, we establish the importance of generating plausible and understandable explanations for model predictions with a special emphasis on algorithm fairness. With the fact that deep learning methods rely on comparatively larger datasets of top-notch quality, this chapter will also highlight the availability of relevant datasets in this space, as well as share pointers to curate one if needed. Even with sufficient data, however, detection problems in this domain are especially challenging since spammers and fake content generators are working tirelessly to evolve their strategies in parallel to the advancement in detection mechanisms. We will further shed some light on some recent and upcoming trends from the aspect of fake news contributors, and critically evaluate how our current state-of-the-art deep learning techniques fare against those. In closing, we will leave readers with some thoughts on future directions for the development of better and smarter fake news detectors.
Both the authors contributed equally.
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Misra, R., Grover, J. (2022). Do Not ‘Fake It Till You Make It’! Synopsis of Trending Fake News Detection Methodologies Using Deep Learning. In: Hong, TP., Serrano-Estrada, L., Saxena, A., Biswas, A. (eds) Deep Learning for Social Media Data Analytics. Studies in Big Data, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-10869-3_12
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