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
- University of Sheffield,
- University of Warwick,
- Queen Mary University of London,
- ,
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewOpen 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.
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-reviewHost publication Subtitle
NAACL HLT 2019Original language
EnglishPages from-to (Number of pages)
Pages 845-854Publication milestones
- Accepted/In press - 01/06/2019
- Published - 07/06/2019
Publication status
Published - 07/06/2019
