Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags

Yunli Wang; Cyril Goutte

doi:10.18653/v1/W17-2702

Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags

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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
Editors:: Tommaso Caselli, Ben Miller, Marieke van Erp, Piek Vossen, Martha Palmer, Eduard Hovy, Teruko Mitamura, David Caswell
Venue:: EventStory
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 10–14
Language:
URL:: https://aclanthology.org/W17-2702/
DOI:: 10.18653/v1/W17-2702
Bibkey:
Cite (ACL):: Yunli Wang and Cyril Goutte. 2017. Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags. In Proceedings of the Events and Stories in the News Workshop, pages 10–14, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):: Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags (Wang & Goutte, EventStory 2017)
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PDF:: https://aclanthology.org/W17-2702.pdf

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@inproceedings{wang-goutte-2017-detecting,
    title = "Detecting Changes in {T}witter Streams using Temporal Clusters of Hashtags",
    author = "Wang, Yunli  and
      Goutte, Cyril",
    editor = "Caselli, Tommaso  and
      Miller, Ben  and
      van Erp, Marieke  and
      Vossen, Piek  and
      Palmer, Martha  and
      Hovy, Eduard  and
      Mitamura, Teruko  and
      Caswell, David",
    booktitle = "Proceedings of the Events and Stories in the News Workshop",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-2702/",
    doi = "10.18653/v1/W17-2702",
    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."
}

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%0 Conference Proceedings
%T Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags
%A Wang, Yunli
%A Goutte, Cyril
%Y Caselli, Tommaso
%Y Miller, Ben
%Y van Erp, Marieke
%Y Vossen, Piek
%Y Palmer, Martha
%Y Hovy, Eduard
%Y Mitamura, Teruko
%Y Caswell, David
%S Proceedings of the Events and Stories in the News Workshop
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F wang-goutte-2017-detecting
%X 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.
%R 10.18653/v1/W17-2702
%U https://aclanthology.org/W17-2702/
%U https://doi.org/10.18653/v1/W17-2702
%P 10-14

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Markdown (Informal)

[Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags](https://aclanthology.org/W17-2702/) (Wang & Goutte, EventStory 2017)

Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags (Wang & Goutte, EventStory 2017)

ACL

Yunli Wang and Cyril Goutte. 2017. Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags. In Proceedings of the Events and Stories in the News Workshop, pages 10–14, Vancouver, Canada. Association for Computational Linguistics.