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Exploiting Context for Rumour Detection in Social Media

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10539)


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.


  • Social media
  • Rumour detection
  • Breaking news
  • Journalism

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  1. 1.

    Collection script available at

  2. 2.

    We use the PyStruct package to implement CRF [21].

  3. 3.

    We used the scikit-learn Python package for these baselines.

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This work has been supported by the PHEME FP7 project (grant No. 611233). Maria Liakata and Rob Procter were also supported by the Alan Turing Institute. We would also like to thank Queen Mary University of London for the use of its MidPlus computational facilities, which was supported by QMUL Research-IT and funded by EPSRC grant EP/K000128/1.

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Correspondence to Arkaitz Zubiaga .

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Zubiaga, A., Liakata, M., Procter, R. (2017). Exploiting Context for Rumour Detection in Social Media. In: Ciampaglia, G., Mashhadi, A., Yasseri, T. (eds) Social Informatics. SocInfo 2017. Lecture Notes in Computer Science(), vol 10539. Springer, Cham.

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