Exploiting Context for Rumour Detection in Social Media

Arkaitz Zubiaga, Maria Liakata, Rob Procter

SocInfo. 2017.

Tools that are able to detect unverified information posted on social media during a news event can help to avoid the spread of rumours that turn out to be false. In this paper we compare a novel approach using Conditional Random Fields that learns from the sequential dynamics of social media posts with the current state-of-the-art rumour detection system, as well as other baselines. In contrast to existing work, our classifier does not need to observe tweets querying the stance of a post to deem it a rumour but, instead, exploits context learned during the event. Our classifier has improved precision and recall over the state-of-the-art classifier that relies on querying tweets, as well as outperforming our best baseline. Moreover, the results provide evidence for the generalisability of our classifier.

@inproceedings{zubiaga2017exploiting,
  title={Exploiting context for rumour detection in social media},
  author={Zubiaga, Arkaitz and Liakata, Maria and Procter, Rob},
  booktitle={Social Informatics: 9th International Conference, SocInfo 2017, Oxford, UK, September 13-15, 2017, Proceedings, Part I 9},
  pages={109--123},
  year={2017},
  organization={Springer}
}