Can Rumour Stance Alone Predict Veracity?
Abstract
Prior manual studies of rumours suggested that crowd stance can give insights into the actual rumour veracity. Even though numerous studies of automatic veracity classification of social media rumours have been carried out, none explored the effectiveness of leveraging crowd stance to determine veracity. We use stance as an additional feature to those commonly used in earlier studies. We also model the veracity of a rumour using variants of Hidden Markov Models (HMM) and the collective stance information. This paper demonstrates that HMMs that use stance and tweets’ times as the only features for modelling true and false rumours achieve F1 scores in the range of 80%, outperforming those approaches where stance is used jointly with content and user based features.- Anthology ID:
- C18-1284
- Volume:
- Proceedings of the 27th International Conference on Computational Linguistics
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Emily M. Bender, Leon Derczynski, Pierre Isabelle
- Venue:
- COLING
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3360–3370
- Language:
- URL:
- https://aclanthology.org/C18-1284
- DOI:
- Cite (ACL):
- Sebastian Dungs, Ahmet Aker, Norbert Fuhr, and Kalina Bontcheva. 2018. Can Rumour Stance Alone Predict Veracity?. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3360–3370, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Can Rumour Stance Alone Predict Veracity? (Dungs et al., COLING 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/C18-1284.pdf