Abstract
Given the re-broadcasts (i.e. retweets) of posts in Twitter, how can we spot fake from genuine user reactions? What will be the tell-tale sign — the connectivity of retweeters, their relative timing, or something else? High retweet activity indicates influential users, and can be monetized. Hence, there are strong incentives for fraudulent users to artificially boost their retweets’ volume. Here, we explore the identification of fraudulent and genuine retweet threads. Our main contributions are: (a) the discovery of patterns that fraudulent activity seems to follow (the “triangles ” and “homogeneity ” patterns, the formation of micro-clusters in appropriate feature spaces); and (b) “RTGen ”, a realistic generator that mimics the behaviors of both honest and fraudulent users. We present experiments on a dataset of more than 6 million retweets crawled from Twitter.
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Giatsoglou, M., Chatzakou, D., Shah, N., Faloutsos, C., Vakali, A. (2015). Retweeting Activity on Twitter: Signs of Deception. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9077. Springer, Cham. https://doi.org/10.1007/978-3-319-18038-0_10
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DOI: https://doi.org/10.1007/978-3-319-18038-0_10
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