A Survey on Social Media Anomaly Detection
January 06, 2016 ยท Declared Dead ยท ๐ SIGKDD Explorations
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Authors
Rose Yu, Huida Qiu, Zhen Wen, Ching-Yung Lin, Yan Liu
arXiv ID
1601.01102
Category
cs.LG: Machine Learning
Cross-listed
cs.SI
Citations
119
Venue
SIGKDD Explorations
Last Checked
3 months ago
Abstract
Social media anomaly detection is of critical importance to prevent malicious activities such as bullying, terrorist attack planning, and fraud information dissemination. With the recent popularity of social media, new types of anomalous behaviors arise, causing concerns from various parties. While a large amount of work have been dedicated to traditional anomaly detection problems, we observe a surge of research interests in the new realm of social media anomaly detection. In this paper, we present a survey on existing approaches to address this problem. We focus on the new type of anomalous phenomena in the social media and review the recent developed techniques to detect those special types of anomalies. We provide a general overview of the problem domain, common formulations, existing methodologies and potential directions. With this work, we hope to call out the attention from the research community on this challenging problem and open up new directions that we can contribute in the future.
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