Abstract
Comments on web news contain controversies that manifest as inter-group agreement-conflicts. Tracking such rapidly evolving controversy could ease conflict resolution or journalist-user interaction. However, this presupposes controversy online-prediction that scales to diverse domains using incidental supervision signals instead of manual labeling. To more deeply interpret comment-controversy model decisions we frame prediction as binary classification and evaluate baselines and multi-task CNNs that use an auxiliary news-genre-encoder. Finally, we use ablation and interpretability methods to determine the impacts of topic, discourse and sentiment indicators, contextual vs. global word influence, as well as genre-keywords vs. per-genre-controversy keywords – to find that the models learn plausible controversy features using only incidentally supervised signals.- Anthology ID:
- W18-6246
- Volume:
- Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Alexandra Balahur, Saif M. Mohammad, Veronique Hoste, Roman Klinger
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 316–321
- Language:
- URL:
- https://aclanthology.org/W18-6246
- DOI:
- 10.18653/v1/W18-6246
- Cite (ACL):
- Nils Rethmeier, Marc Hübner, and Leonhard Hennig. 2018. Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 316–321, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs (Rethmeier et al., WASSA 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W18-6246.pdf