Publication
Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs
Nils Rethmeier; Marc Hübner; Leonhard Hennig
In: The 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis - Proceedings of the Workshop. Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA-2018), located at EMNLP 2018, October 31 - November 4, Brussels, Belgium, Pages 316-321, Association for Computational Linguistics, 10/2018.
Abstract
Comments on web news contain controversies
that manifest as inter-group agreementconflicts.
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 commentcontroversy
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-genrecontroversy
keywords – to find that the models
learn plausible controversy features using only
incidentally supervised signals.