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        <title>Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs</title>
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        <namePart type="given">Nils</namePart>
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            <title>Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis</title>
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            <namePart type="given">Alexandra</namePart>
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            <namePart type="given">Saif</namePart>
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            <namePart type="given">Veronique</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs
%A Rethmeier, Nils
%A Hübner, Marc
%A Hennig, Leonhard
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F rethmeier-etal-2018-learning
%X 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.
%R 10.18653/v1/W18-6246
%U https://aclanthology.org/W18-6246/
%U https://doi.org/10.18653/v1/W18-6246
%P 316-321

Markdown (Informal)

[Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs](https://aclanthology.org/W18-6246/) (Rethmeier et al., WASSA 2018)

• Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs (Rethmeier et al., WASSA 2018)

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.