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- W4285281143 abstract "Data on social media can take many forms, such as reviews, news, images, and videos. Some entities try to take advantage of this rich data to make data-driven decisions. However, fake data can be strategically generated to mislead. A prominent example is the way TikTok users manipulated the number of people who would be attending Donald Trump’s rally in Tulsa, Oklahoma, in June 2020. The literature on machine learning has examined how to detect whether a single piece of data is fake based on its features. However, data on social media can be a dynamic stream in which no single piece of data can be determined to be fake by its own features. Looking at the whole stream provides more information. In this chapter, we discuss the tension between a sender of dynamic fake data and its receiver. This chapter presents a continuous-time game-theoretic model with asymmetric information and a potentially infinite horizon in order to capture that the receiver is initially unaware of the presence of fake data but learns about it later in the game. We thus present a methodology based on this model to detect dynamic fake data. This methodology complements previous studies that have proposed other, piece-by-piece approaches to analyze such data." @default.
- W4285281143 created "2022-07-14" @default.
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- W4285281143 date "2022-01-01" @default.
- W4285281143 modified "2023-09-26" @default.
- W4285281143 title "Dynamic Deception Using Social Media" @default.
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- W4285281143 doi "https://doi.org/10.1007/978-981-19-1524-6_11" @default.
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