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WIREs Data Mining Knowl Discov
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Detecting deceptive engagement in social media by temporal pattern analysis of user behaviors: a survey

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Deceptive engagement in social media, such as spamming, commenting, or rating with automatic scripts, spreading fabricated facts, seriously affects users’ trust on online services. Given the large volumes of information generated by users, effectively spotting users involved such deceptive engagement has become a challenging problem. Recent research has shown that techniques for analyzing temporal behavioral patterns are critical to address such problem. In this study, we survey recent advances in these techniques. We first summarize three approaches to model temporal information. Then, by using representative application examples, we discuss recent approaches with respect to their applications to real‐world large‐scale social media. With a focus on the temporal perspective, we then discuss advantages and challenges of each approach. WIREs Data Mining Knowl Discov 2017, 7:e1210. doi: 10.1002/widm.1210 This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Technologies > Prediction
Examples of attributed networks in social media. (a) User–user network, Facebook. (b) User–action sequence, Renren. (c) User–content network, Prosper.com. (d) User–content network, YouTube.
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An example of suspicious synchronized retweeting behaviors, where blue dot and red cross represent two IP addresses.
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Clustering on bipartite network. (a) User–content bipartite network. (b) Converted user–user network.
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Role transition.
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Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining
Fundamental Concepts of Data and Knowledge > Knowledge Representation
Technologies > Prediction

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