mlsanityData to 7 Oct 2026

A Survey on Social Media Anomaly Detection

arXiv 1601.01102 ↗First posted 6 Jan 2016cs.LG cs.SIcited by 17

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Abstract (arXiv)

Social media anomaly detection is of critical importance to prevent malicious activities such as bullying, terrorist attack planning, and fraud information dissemination. With the recent popularity of social media, new types of anomalous behaviors arise, causing concerns from various parties. While a large amount of work have been dedicated to traditional anomaly detection problems, we observe a surge of research interests in the new realm of social media anomaly detection. In this paper, we present a survey on existing approaches to address this problem. We focus on the new type of anomalous phenomena in the social media and review the recent developed techniques to detect those special types of anomalies. We provide a general overview of the problem domain, common formulations, existing methodologies and potential directions. With this work, we hope to call out the attention from the research community on this challenging problem and open up new directions that we can contribute in the future.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jan 2016.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 5; 2025: 2; 2026: 2.

17 papers.

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  1. 2021A Comprehensive Survey on Graph Anomaly Detection with Deep Learningcited by 230
  2. 2023GAD-NR: Graph Anomaly Detection via Neighborhood Reconstructioncited by 44
  3. 2024Deep Graph Anomaly Detection: A Survey and New Perspectivescited by 40
  4. 2022A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systemscited by 20
  5. 2024Anomaly Detection in Dynamic Graphs: A Comprehensive Surveycited by 18
  6. 2022Detecting Rumours with Latency Guarantees using Massive Streaming Datacited by 12
  7. 2022ARISE: Graph Anomaly Detection on Attributed Networks via Substructure Awarenesscited by 8
  8. 2021TOD: GPU-accelerated Outlier Detection via Tensor Operationscited by 6
  9. 2023Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learningcited by 6
  10. 2023Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approachcited by 3
  11. 2024Anomaly Detection in Graph Structured Data: A Surveycited by 3
  12. 2025HLSAD: Hodge Laplacian-based Simplicial Anomaly Detectioncited by 2
5 more
  1. 2024Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detectioncited by 0
  2. 2024Foundations for Unfairness in Anomaly Detection -- Case Studies in Facial Imaging Datacited by 0
  3. 2025Human-Centric Community Detection in Hybrid Metaverse Networks with Integrated AI Entitiescited by 0
  4. 2026Balanced Anomaly-guided Ego-graph Diffusion Model for Inductive Graph Anomaly Detectioncited by 0
  5. 2026Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detectioncited by 0