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• W3019320019 endingPage "102250" @default.
• W3019320019 startingPage "102250" @default.
• W3019320019 abstract "Social media bots (automated accounts) attacks are organized crimes that pose potential threats to public opinion, democracy, public health, stock market and other disciplines. While researchers are building many models to detect social media bot accounts, attackers, on the other hand, evolve their bots to evade detection. This everlasting cat and mouse game makes this field vibrant and demands continuous development. To guide and enhance future solutions, this work provides an overview of social media bots attacks, current detection methods and challenges in this area. To the best of our knowledge, this paper is the first systematic review based on a predefined search strategy, which includes literature concerned about social media bots detection methods, published between 2010 and 2019. The results of this review include a refined taxonomy of detection methods, a highlight of the techniques used to detect bots in social media and a comparison between current detection methods. Some of the gaps identified by this work are: the literature mostly focus on Twitter platform only and rarely use methods other than supervised machine learning, most of the public datasets are not accurate or large enough, integrated systems and real-time detection are required, and efforts to spread awareness are needed to arm legitimate users with knowledge." @default.
• W3019320019 created "2020-05-01" @default.
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• W3019320019 date "2020-07-01" @default.
• W3019320019 modified "2023-10-14" @default.
• W3019320019 title "Detection of Bots in Social Media: A Systematic Review" @default.
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• W3019320019 doi "https://doi.org/10.1016/j.ipm.2020.102250" @default.
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