Social media anomaly detection: Challenges and solutions

Yan Liu, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

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 publicationWSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages817-818
Number of pages2
ISBN (Electronic)9781450346757
DOIs
StatePublished - Feb 2 2017
Externally publishedYes
Event10th ACM International Conference on Web Search and Data Mining, WSDM 2017 - Cambridge, United Kingdom
Duration: Feb 6 2017Feb 10 2017

Publication series

NameWSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining

Conference

Conference10th ACM International Conference on Web Search and Data Mining, WSDM 2017
Country/TerritoryUnited Kingdom
CityCambridge
Period02/6/1702/10/17

Keywords

  • Anomaly detection
  • Social media analysis

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