Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations
Arkaitz Zubiaga, Elena Kochkina, Maria Liakata, Rob Procter, Michal Lukasik
Abstract
Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial interest. Here we introduce a novel approach that makes use of the sequence of transitions observed in tree-structured conversation threads in Twitter. The conversation threads are formed by harvesting users’ replies to one another, which results in a nested tree-like structure. Previous work addressing the stance classification task has treated each tweet as a separate unit. Here we analyse tweets by virtue of their position in a sequence and test two sequential classifiers, Linear-Chain CRF and Tree CRF, each of which makes different assumptions about the conversational structure. We experiment with eight Twitter datasets, collected during breaking news, and show that exploiting the sequential structure of Twitter conversations achieves significant improvements over the non-sequential methods. Our work is the first to model Twitter conversations as a tree structure in this manner, introducing a novel way of tackling NLP tasks on Twitter conversations.- Anthology ID:
- C16-1230
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 2438–2448
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/C16-1230/
- DOI:
- Cite (ACL):
- Arkaitz Zubiaga, Elena Kochkina, Maria Liakata, Rob Procter, and Michal Lukasik. 2016. Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2438–2448, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations (Zubiaga et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/C16-1230.pdf