2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2016)
San Francisco, CA, USA
Aug. 18, 2016 to Aug. 21, 2016
Janani Kalyanam , University of California, San Diego
Sumithra Velupillai , KTH, Stockholm and King's College, London
Mike Conway , University of Utah, Salt Lake City
Gert Lanckriet , University of California, San Diego
The problem of detecting events from content published on microblogs has garnered much interest in recent times. In this paper, we address the questions of what happens after the outbreak of an event in terms of how the event gradually progresses and attains each of its milestones, and how it eventually dissipates. We propose a model based approach to capture the gradual unfolding of an event over time. This enables the model to automatically produce entire timeline trajectories of events from the time of their outbreak to their disappearance. We apply our model on the Twitter messages collected about Ebola during the 2014 outbreak and obtain the progression timelines of several events that occurred during the outbreak. We also compare our model to several existing topic modeling and event detection baselines in literature to demonstrate its efficiency.
Mathematical model, Event detection, Optimization, Vocabulary, Adaptation models, Sparse matrices, Symmetric matrices
J. Kalyanam, S. Velupillai, M. Conway and G. Lanckriet, "From event detection to storytelling on microblogs," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), San Francisco, CA, USA, 2016, pp. 437-442.