Exploiting Context for Rumour Detection in Social Media

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10539)

Abstract

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.

Keywords

Social media Rumour detection Breaking news Journalism 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of WarwickCoventryUK
  2. 2.Alan Turing InstituteLondonUK

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