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SemEval-2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours

  • Genevieve Gorrell
    ,
  • Elena Kochkina
    ,
  • Maria Liakata
    ,
  • Ahmet Aker
    ,
  • Arkaitz Zubiaga
    ,
  • Kalina Bontcheva
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review

Open access

Abstract

Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the danger of ``fake news'' has become a mainstream concern. However automated support for rumour verification remains in its infancy.
It is therefore important that a shared task in this area continues to provide a focus for effort, which is likely to increase. Rumour verification is characterised by the need to consider evolving conversations and news updates to reach a verdict on a rumour's veracity. As in RumourEval 2017 we provided a dataset of dubious posts and ensuing conversations in social media, annotated both for stance and veracity. The social media rumours stem from a variety of breaking news stories and the dataset is expanded to include Reddit as well as new Twitter posts. There were two concrete tasks; rumour stance prediction and rumour verification, which we present in detail along with results achieved by participants. We received 22 system submissions (a 70\% increase from RumourEval 2017) many of which used state-of-the-art methodology to tackle the challenges involved.

Publication Information

Output type

Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review

Host publication Subtitle

NAACL HLT 2019

Original language

English

Pages from-to (Number of pages)

Pages 845-854

Publication milestones

  • Accepted/In press - 01/06/2019
  • Published - 07/06/2019

Publication status

Published - 07/06/2019

Publisher

Association for Computational Linguistics, United States
978-1-950737-06-2

Host publication title

Proceedings of the 13th International Workshop on Semantic Evaluation