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-review
Open access
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
Publisher
Association for Computational Linguistics, United StatesISBN (Print)
978-1-950737-06-2Host publication title
Proceedings of the 13th International Workshop on Semantic EvaluationAbstract
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.