GLAD: group anomaly detection in social media analysis

  title={GLAD: group anomaly detection in social media analysis},
  author={Rose Yu and Xinran He and Yan Liu},
  journal={Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining},
  • Rose Yu, Xinran He, Yan Liu
  • Published 24 August 2014
  • Computer Science
  • Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
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… 

Figures and Tables from this paper

A Survey on Social Media Anomaly Detection

A survey on existing approaches to address the new type of anomalous phenomena in the social media and review the recent developed techniques to detect those special types of anomalies is presented.

Multi-View Group Anomaly Detection

This paper formalizes this group anomaly detection issue, and proposes a novel non-parametric bayesian model, named Multi-view Group Anomaly Detection (MGAD), which first discovers the distribution of groups or topics in each view, then detects group anomalies effectively.

Group Anomaly Detection: Past Notions, Present Insights, and Future Prospects

The authors decided to survey existing group anomaly detection techniques because there is a need to consider group anomalies for mitigation of risks, prevention of malicious collaborative activities, and other interesting explanatory insights by identifying groups that are not consistent with regular group patterns.

Social Media Anomaly Detection: Challenges and Solutions

This tutorial surveys existing work on social media anomaly detection, focusing on the new anomalous phenomena in social media and most recent techniques to detect those special types of anomalies.

Anomaly Detection in Microblogging via Co-Clustering

An innovative framework of anomaly detection based on bipartite graph and co-clustering that can detect individual and group anomalies with high accuracy on a Sina Weibo dataset is proposed.


  • P. RajRakhi Garg
  • Computer Science
    Indian Journal of Computer Science and Engineering
  • 2020
The algorithm for anomaly detection using graph mining techniques has been categorized on the basis of different characteristics of anomalies, and the types of anomalies generated to help the researchers and the scientists working in this area to find the solutions for problems associated with various algorithms.

ATD: Anomalous Topic Discovery in High Dimensional Discrete Data

The method can accurately detect anomalous topics and salient features under each such topic in a synthetic data set and two real-world text corpora and achieves better performance compared to both standard group AD and individual AD techniques.

Social velocity based spatio-temporal anomalous daily activity discovery of social media users

A novel method that discovers anomalous daily activities with respect to spatio-temporal information of social media datasets is proposed and two novel algorithms are proposed that use proposed method and interest measure and experimentally evaluated on a real Twitter dataset.

Identifying Coordinated Accounts on Social Media through Hidden Influence and Group Behaviours

A generative model which jointly models account activities and hidden group behaviours based on Temporal Point Processes and Gaussian Mixture Model is proposed and it is found that the average influence between coordinated account pairs is the highest.

Absenteeism Detection in Social Media

This paper presents the first study, to the knowledge, that models absenteeism and uses detected absenteeism instances as a basis for event detection in location-based social networks such as Twitter.



Group Anomaly Detection using Flexible Genre Models

The Flexible Genre Model (FGM) is proposed, designed to characterize data groups at both the point level and the group level so as to detect various types of group anomalies.

Hierarchical Probabilistic Models for Group Anomaly Detection

Generative models for detecting group anomalies, which are larger scale phenomena that only become apparent when groups of points are considered, are proposed.

Bayesian anomaly detection methods for social networks

The first stage uses simple, conjugate Bayesian models for discrete time counting processes to track the pairwise links of all nodes in the graph to assess normality of behavior, and the second stage applies standard network inference tools on a greatly reduced subset of potentially anomalous nodes.

One-Class Support Measure Machines for Group Anomaly Detection

It is shown that various types of VKDEs can be considered as solutions to a class of regularization problems studied in this paper, bridging the gap between large-margin methods and kernel density estimators.

ArnetMiner: extraction and mining of academic social networks

The architecture and main features of the ArnetMiner system, which aims at extracting and mining academic social networks, are described and a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues is proposed.

Anomaly detection: A survey

This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.

Probabilistic topic models with biased propagation on heterogeneous information networks

This paper proposes a novel topic model with biased propagation (TMBP) algorithm to directly incorporate heterogeneous information network with topic modeling in a unified way and extensively evaluates the proposed approach and compares to the state-of-the-art techniques on several datasets.

Community detection in graphs

Latent Dirichlet Allocation

Outlier Detection : A Survey

This survey provides a comprehensive overview of existing outlier detection techniques by classifying them along different dimensions.