Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs

Nils Rethmeier, Marc Hübner, Leonhard Hennig


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
Venues:
EMNLP | WASSA | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
316–321
Language:
URL:
https://www.aclweb.org/anthology/W18-6246
DOI:
10.18653/v1/W18-6246
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-6246.pdf