Journal
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
Volume 10, Issue 2, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2811268
Keywords
Anomaly Detection; Social Media Analysis; Hierarchical Bayes Modeling; Group anomaly; topic modeling; community detection
Funding
- U.S. Defense Advanced Research Projects Agency (DARPA) under the Anomaly Detection at Multiple Scales (ADAMS) program [W911NF-11-C-0200]
- NSF [IIS-1134990]
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Traditional anomaly detection on social media mostly focuses on individual point anomalies while anomalous phenomena usually occur in groups. Therefore, it is valuable to study the collective behavior of individuals and detect group anomalies. Existing group anomaly detection approaches rely on the assumption that the groups are known, which can hardly be true in real world social media applications. In this article, we take a generative approach by proposing a hierarchical Bayes model: Group Latent Anomaly Detection (GLAD) model. GLAD takes both pairwise and point-wise data as input, automatically infers the groups and detects group anomalies simultaneously. To account for the dynamic properties of the social media data, we further generalize GLAD to its dynamic extension d-GLAD. We conduct extensive experiments to evaluate our models on both synthetic and real world datasets. The empirical results demonstrate that our approach is effective and robust in discovering latent groups and detecting group anomalies.
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