research-article

GLAD: Group Anomaly Detection in Social Media Analysis

Authors:

Yan LiuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 10, Issue 2

Article No.: 18, Pages 1 - 22

https://doi.org/10.1145/2811268

Published: 26 October 2015 Publication History

Abstract

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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Cited By

Hu BWang XZhou PDu L(2024)Multi-view Outlier Detection via Graphs DenoisingInformation Fusion10.1016/j.inffus.2023.102012101(102012)Online publication date: Jan-2024
https://doi.org/10.1016/j.inffus.2023.102012
Najafi PCheng FMeinel C(2024)HEOD: Human-assisted Ensemble Outlier Detection for cybersecurityComputers & Security10.1016/j.cose.2024.104040146(104040)Online publication date: Nov-2024
https://doi.org/10.1016/j.cose.2024.104040
Lazebnik TIny O(2024)Temporal graphs anomaly emergence detection: benchmarking for social media interactionsApplied Intelligence10.1007/s10489-024-05821-354:23(12347-12356)Online publication date: 12-Sep-2024
https://doi.org/10.1007/s10489-024-05821-3
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Index Terms

GLAD: Group Anomaly Detection in Social Media Analysis
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 10, Issue 2

October 2015

291 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/2835206

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

Issue’s Table of Contents

Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2015

Accepted: 01 July 2015

Received: 01 January 2015

Published in TKDD Volume 10, Issue 2

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U.S. Defense Advanced Research Projects Agency (DARPA) under the Anomaly Detection at Multiple Scales (ADAMS) program
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Cited By

Hu BWang XZhou PDu L(2024)Multi-view Outlier Detection via Graphs DenoisingInformation Fusion10.1016/j.inffus.2023.102012101(102012)Online publication date: Jan-2024
https://doi.org/10.1016/j.inffus.2023.102012
Najafi PCheng FMeinel C(2024)HEOD: Human-assisted Ensemble Outlier Detection for cybersecurityComputers & Security10.1016/j.cose.2024.104040146(104040)Online publication date: Nov-2024
https://doi.org/10.1016/j.cose.2024.104040
Lazebnik TIny O(2024)Temporal graphs anomaly emergence detection: benchmarking for social media interactionsApplied Intelligence10.1007/s10489-024-05821-354:23(12347-12356)Online publication date: 12-Sep-2024
https://doi.org/10.1007/s10489-024-05821-3
Pratelli MPetrocchi MSaracco FDe Nicola R(2023)Swinging in the States: Does disinformation on Twitter mirror the US presidential election system?Companion Proceedings of the ACM Web Conference 202310.1145/3543873.3587638(1395-1403)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587638
Huang LZhu YGao YLiu TChang CLiu CTang YWang C(2023)Hybrid-Order Anomaly Detection on Attributed NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.311784235:12(12249-12263)Online publication date: 1-Dec-2023
https://doi.org/10.1109/TKDE.2021.3117842
Ahmed ULin JSrivastava G(2023)Deep Fuzzy Contrast-Set Deviation Point Representation and Trajectory DetectionIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2022.319787631:2(571-581)Online publication date: Feb-2023
https://doi.org/10.1109/TFUZZ.2022.3197876
Chu GWang JQi QSun HTao SYang HLiao JHan Z(2023)Exploiting Spatial-Temporal Behavior Patterns for Fraud Detection in Telecom NetworksIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2022.322879720:6(4564-4577)Online publication date: Nov-2023
https://doi.org/10.1109/TDSC.2022.3228797
Sharma KSingh A(2023)A Systematic Review: Detection of Anomalies in Social Networks2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)10.1109/ICSCDS56580.2023.10104612(1470-1476)Online publication date: 23-Mar-2023
https://doi.org/10.1109/ICSCDS56580.2023.10104612
Sotiropoulos KZhao LLiang PAkoglu L(2023)ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386245(865-874)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386245
Le HViviani JFauzi F(2023)Why do banks fail? An investigation via text miningCogent Economics & Finance10.1080/23322039.2023.225127211:2Online publication date: 3-Sep-2023
https://doi.org/10.1080/23322039.2023.2251272
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