Social Media Anomaly Detection: Challenges and Solutions


Yan Liu, Computer Science Department, University of Southern California

Sanjay Chawla, School of Information Technologies, The University of Sydney





Anomaly detection problem 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 large of amount 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 the recent developed 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.


Location and Time


Location: Committee Room 1

Time: 9:00am – 12:30pm Feb 6, 2017


Instructor's Short Bio

Dr. Sanjay Chawla is the Professor in the Faculty of Engineering and IT, University of Sydney. He is currently on leave as Principal Scientist at the Qatar Computing Research Institute. He was an academic visitor at Yahoo! Research in 2012. Sanjay’s area of research is Data Mining and Machine Learning with a specialization in spatio-temporal data mining, outlier detection, class imbalanced classification and adversarial learning. He is a co-author on a popular text in spatial database management systems: “ Spatial Databases: A Tour” which has been translated into Chinese and Russian. His work has been recognized by several best paper awards including in leading conferences such as SIAM International Conference in Data Mining (2006) and IEEE International Conference in Data Mining (2010). Sanjay serves on the Editorial Board of IEEE TKDE and DMKD. He served as PC Chair of PAKDD in 2012


Dr. Yan Liu is an associate professor in Computer Science Department at University of Southern California. Before joining USC, she was a Research Staff Member at IBM Research in 2006-2010. She received her Ph.D. degree from Carnegie Mellon University in 2007.  Her research interest is data mining and machine learning with applications to social media, biology and climate science. She has received several awards, including NSF CAREER Award, Okawa Foundation Research Award, ACM Dissertation Award Honorable Mention, Best Paper Award in SIAM Data Mining Conference, Yahoo! Faculty Award and the winner of several data mining competitions, such as KDD Cup and INFORMS data mining competition. She has published over 10 referred articles on temporal causal models for time series data in top conferences, such as KDD, ICML, ICDM, SDM and AAAI, and given invited talks on the topic in many institutions and industrial research labs.





Supplementary Materials


Slides: [PDF]


Survey paper: [PDF]