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        <title>Fact vs. Opinion: the Role of Argumentation Features in News Classification</title>
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        <namePart type="given">Tariq</namePart>
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    <abstract>A 2018 study led by the Media Insight Project showed that most journalists think that a clearmarking of what is news reporting and what is commentary or opinion (e.g., editorial, op-ed)is essential for gaining public trust. We present an approach to classify news articles into newsstories (i.e., reporting of factual information) and opinion pieces using models that aim to sup-plement the article content representation with argumentation features. Our hypothesis is thatthe nature of argumentative discourse is important in distinguishing between news stories andopinion articles. We show that argumentation features outperform linguistic features used previ-ously and improve on fine-tuned transformer-based models when tested on data from publishersunseen in training. Automatically flagging opinion pieces vs. news stories can aid applicationssuch as fact-checking or event extraction.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.540</identifier>
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        <date>2020-12</date>
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%0 Conference Proceedings
%T Fact vs. Opinion: the Role of Argumentation Features in News Classification
%A Alhindi, Tariq
%A Muresan, Smaranda
%A Preotiuc-Pietro, Daniel
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F alhindi-etal-2020-fact
%X A 2018 study led by the Media Insight Project showed that most journalists think that a clearmarking of what is news reporting and what is commentary or opinion (e.g., editorial, op-ed)is essential for gaining public trust. We present an approach to classify news articles into newsstories (i.e., reporting of factual information) and opinion pieces using models that aim to sup-plement the article content representation with argumentation features. Our hypothesis is thatthe nature of argumentative discourse is important in distinguishing between news stories andopinion articles. We show that argumentation features outperform linguistic features used previ-ously and improve on fine-tuned transformer-based models when tested on data from publishersunseen in training. Automatically flagging opinion pieces vs. news stories can aid applicationssuch as fact-checking or event extraction.
%R 10.18653/v1/2020.coling-main.540
%U https://aclanthology.org/2020.coling-main.540
%U https://doi.org/10.18653/v1/2020.coling-main.540
%P 6139-6149

Markdown (Informal)

[Fact vs. Opinion: the Role of Argumentation Features in News Classification](https://aclanthology.org/2020.coling-main.540) (Alhindi et al., COLING 2020)

• Fact vs. Opinion: the Role of Argumentation Features in News Classification (Alhindi et al., COLING 2020)

ACL

• Tariq Alhindi, Smaranda Muresan, and Daniel Preotiuc-Pietro. 2020. Fact vs. Opinion: the Role of Argumentation Features in News Classification. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6139–6149, Barcelona, Spain (Online). International Committee on Computational Linguistics.