@InProceedings{wang-goutte:2017:EventStory,
  author    = {Wang, Yunli  and  Goutte, Cyril},
  title     = {Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags},
  booktitle = {Proceedings of the Events and Stories in the News Workshop},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {10--14},
  abstract  = {Detecting events from social media data has important applications in public
	security, political issues, and public health. Many studies have focused on
	detecting specific or unspecific events from Twitter streams. However, not much
	attention has been paid to detecting changes, and their impact, in online
	conversations related to an event. We propose methods for detecting such
	changes, using clustering of temporal profiles of hashtags, and three change
	point detection algorithms. The methods were tested on two Twitter datasets:
	one covering the 2014 Ottawa shooting event, and one covering the Sochi winter
	Olympics. We compare our approach to a baseline consisting of detecting change
	from raw counts in the conversation. We show that our method produces large
	gains in change detection accuracy on both datasets.},
  url       = {http://www.aclweb.org/anthology/W17-2702}
}