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, Leon Derczynski

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the 13th International Workshop on Semantic Evaluation : NAACL HLT 2019
PublisherAssociation for Computational Linguistics
Publication date7 Jun 2019
ISBN (Print)978-1-950737-06-2
Publication statusPublished - 7 Jun 2019


  • automated claim validation
  • fake news
  • rumour verification
  • social media
  • dataset annotation


Dive into the research topics of 'SemEval-2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours'. Together they form a unique fingerprint.

Cite this