Social media anomaly detection: Challenges and solutions

Yan Liu; Sanjay Chawla

doi:10.1145/2783258.2789990

Social media anomaly detection: Challenges and solutions

Yan Liu, Sanjay Chawla

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 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 language	English
Title of host publication	KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	2317-2318
Number of pages	2
ISBN (Electronic)	9781450336642
DOIs	https://doi.org/10.1145/2783258.2789990
State	Published - Aug 10 2015
Externally published	Yes
Event	21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia Duration: Aug 10 2015 → Aug 13 2015

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume	2015-August

Conference

Conference	21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
Country/Territory	Australia
City	Sydney
Period	08/10/15 → 08/13/15

Keywords

Anomaly detection
Social media analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/2783258.2789990

Cite this

Liu, Y., & Chawla, S. (2015). Social media anomaly detection: Challenges and solutions. In KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2317-2318). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2015-August). Association for Computing Machinery. https://doi.org/10.1145/2783258.2789990

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title = "Social media anomaly detection: Challenges and solutions",

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.",

keywords = "Anomaly detection, Social media analysis",

author = "Yan Liu and Sanjay Chawla",

year = "2015",

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language = "English",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

publisher = "Association for Computing Machinery",

pages = "2317--2318",

booktitle = "KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining",

note = "21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 ; Conference date: 10-08-2015 Through 13-08-2015",

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Liu, Y & Chawla, S 2015, Social media anomaly detection: Challenges and solutions. in KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 2015-August, Association for Computing Machinery, pp. 2317-2318, 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015, Sydney, Australia, 08/10/15. https://doi.org/10.1145/2783258.2789990

Social media anomaly detection: Challenges and solutions. / Liu, Yan; Chawla, Sanjay.
KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2015. p. 2317-2318 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2015-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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ER -

Social media anomaly detection: Challenges and solutions

Abstract

Publication series

Conference

Keywords

UN SDGs

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