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.

研究了基于个体点异常的传统社交媒体的异 常检测方法，提出了一种层次贝叶斯模型 —— 组潜在异常检测（GLAD）模型，可以自动推断组并同时检测组异常。将 GLAD 进一步推广到其动态扩展 d-GLAD，以识别潜在群体和检测异常。