TY - GEN
T1 - Social media anomaly detection
AU - Liu, Yan
AU - Chawla, Sanjay
N1 - Funding Information:
Yan Liu's research was partly sponsored by the NSF research grants IIS-1134990 and IIS-1254206, and U.S. Defense Advanced Research Projects Agency (DARPA) under Anomaly Detection at Multiple Scales (ADAMS) program and Social Media in Strategic Communication (SMISC) program. Sanjay Chawla's research is partially supported by the Australian Research Council, CapialMarkets CRC, CRC for Alertness, Sleep and Productivity. The views and conclusions are those of the authors and should not be interpreted as representing the official policies of the funding agency, or the U.S. Government.
Publisher Copyright:
© 2017 ACM.
PY - 2017/2/2
Y1 - 2017/2/2
N2 - 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.
AB - 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.
KW - Anomaly detection
KW - Social media analysis
UR - http://www.scopus.com/inward/record.url?scp=85015309404&partnerID=8YFLogxK
U2 - 10.1145/3018661.3022757
DO - 10.1145/3018661.3022757
M3 - Conference contribution
AN - SCOPUS:85015309404
T3 - WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
SP - 817
EP - 818
BT - WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining
PB - Association for Computing Machinery, Inc
Y2 - 6 February 2017 through 10 February 2017
ER -