Social media anomaly detection: Challenges and solutions

Yan Liu, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

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 body of work haven 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 tutorial, we survey 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. We aim to provide a general overview of the problem domain, common formulations, existing methodologies and future directions.

Original languageEnglish
Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages2317-2318
Number of pages2
Volume2015-August
ISBN (Print)9781450336642
DOIs
Publication statusPublished - 10 Aug 2015
Externally publishedYes
Event21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia
Duration: 10 Aug 201513 Aug 2015

Other

Other21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
CountryAustralia
CitySydney
Period10/8/1513/8/15

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Keywords

  • Anomaly detection
  • Social media analysis

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Liu, Y., & Chawla, S. (2015). Social media anomaly detection: Challenges and solutions. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. 2015-August, pp. 2317-2318). Association for Computing Machinery. https://doi.org/10.1145/2783258.2789990