Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags

Yunli Wang, Cyril Goutte


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.
Anthology ID:
W17-2702
Volume:
Proceedings of the Events and Stories in the News Workshop
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venues:
EventStory | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10–14
Language:
URL:
https://www.aclweb.org/anthology/W17-2702
DOI:
10.18653/v1/W17-2702
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2702.pdf