Friday, February 3, 2017


Topic: A Framework for Real-Time Event Detection for Emergency Situations using Social Media Streams


Speaker: Dr. Vijay V. Raghavan


Abstract: In this presentation, we propose an event detection approach to aid in real-time event detection. Social media generates information about news and events in real-time. Given the vast amount of data available and the rate of information propagation, reliably identifying events can be a challenge. Most state of the art techniques are post hoc techniques, which detect an event after it happened. Our goal is to detect the onset of an event as it is happening, using the user-generated information from Twitter streams. To achieve this goal, we use a discriminative model to identify a sudden change in the pattern of conversations over time. We also use a topic evolution model to identify credible events and propose an approach to eliminate random noise that is prevalent in many of the existing topic detection approaches. The simplicity of our proposed approach allows us to perform fast and efficient event detection, permitting discovery of events within minutes of the first conversation relating to an event started. We also show that this approach is applicable for other social media datasets to detect change over the longer periods of time.

We extend the proposed event detection approach to incorporate information from multiple data sources with different velocity and volume. We study the event clusters generated from event detection approach for changes in events over time. We also propose and evaluate a location detection approach to identify the location of a user or an event based on tweets related to them.

Biosketch: Dr. Vijay Raghavan is the Alfred and Helen Lamson/ BoRSF Endowed Professor in Computer Science at the Center for Advanced Computer Studies and the Director of the NSF-sponsored Industry/ University Cooperative Research Center for Visual and Decision Informatics. As the director, he co-ordinates several multi-institutional, industry-driven research projects and manages a budget of over $750K/year. His research interests are in data mining, information retrieval, machine learning and Internet computing. He has published over 275 peer-reviewed research papers- appearing in top-level journals and proceedings- that cumulatively accord him an h-index of 35, based on citations at Google Scholar. He has served as major advisor for 29 doctoral students. Besides substantial technical expertise, Dr. Raghavan has vast experience managing interdisciplinary and multi- institutional collaborative projects. He has also directed industry-sponsored research, on projects pertaining to Neuro-imaging based dementia detection and Literature-based biomedical hypotheses generation, respectively, for GE Healthcare and Araicom Research L.L.C.