Abstract
Multiple identity deception, called as sockpuppet, has been commonly used in online social media to spread rumours, publish hate speeches or evade censors. Current works are continually making efforts to detect sockpuppets based on verbal, non-verbal or network-structure features. Network structure has attracted much attention, while the time series dynamic characteristic of sockpuppet network has not been considered. With our observation, after being blocked, a puppetmaster tends to recover previous social relationships as soon as possible to maintain the propagation influence. The earlier the relationship is recovered, the more important it is. To take advantage of this dynamic nature, a time-series sockpuppet detection method is proposed. We first design a weight representation method to record the dynamic growth of sockpuppet’s social relationships and then transfer sockpuppets detection to a similarity time-series analysis problem. The experiments on two real-world datasets of Sina Weibo demonstrate that our method obtains excellent detection performance, significantly outperforming previous methods.
W. Zhou and J. Wang—These authors contributed equally to the work.
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Acknowledgment
This research is supported in part by the National Key Research and Development Program of China (No. 2017YFB1010000).
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Zhou, W., Wang, J., Lin, J., Li, J., Han, J., Hu, S. (2019). A Time-Series Sockpuppet Detection Method for Dynamic Social Relationships. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11446. Springer, Cham. https://doi.org/10.1007/978-3-030-18576-3_3
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