Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter

Michal Lukasik, P. K. Srijith, Duy Vu, Kalina Bontcheva, Arkaitz Zubiaga, Trevor Cohn

ACL. 2016.

Classification of temporal textual data sequences is a common task in various domains such as social media and the Web. In this paper we propose to use Hawkes Processes for classifying sequences of temporal textual data, which exploit both temporal and textual information. Our experiments on rumour stance classification on four Twitter datasets show the importance of using the temporal information of tweets along with the textual content.

@inproceedings{lukasik2016hawkes,
  title={Hawkes processes for continuous time sequence classification: an application to rumour stance classification in twitter},
  author={Lukasik, Michal and Srijith, PK and Vu, Duy and Bontcheva, Kalina and Zubiaga, Arkaitz and Cohn, Trevor},
  booktitle={Proceedings of 54th Annual Meeting of the Association for Computational Linguistics},
  pages={393--398},
  year={2016},
  organization={Association for Computational Linguistics}
}