  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.
