GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstract
This addresses the challenge of identifying anomalous collective behaviors in social media for applications like security or trend analysis, but it is incremental as it builds on prior group anomaly detection work.
The paper tackles the problem of detecting group anomalies in social media, where existing methods assume known groups, by proposing GLAD and d-GLAD models that automatically infer groups and detect anomalies, showing effectiveness and robustness in experiments.
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 paper, we take a generative approach by proposing a hierarchical Bayes model: Group Latent Anomaly Detection (GLAD) model. GLAD takes both pair-wise 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.