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        <title>Predicting Political Orientation in News with Latent Discourse Structure to Improve Bias Understanding</title>
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        <namePart type="given">Nicolas</namePart>
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    <abstract>With the growing number of information sources, the problem of media bias becomes worrying for a democratic society. This paper explores the task of predicting the political orientation of news articles, with a goal of analyzing how bias is expressed. We demonstrate that integrating rhetorical dimensions via latent structures over sub-sentential discourse units allows for large improvements, with a +7.4 points difference between the base LSTM model and its discourse-based version, and +3 points improvement over the previous BERT-based state-of-the-art model. We also argue that this gives a new relevant handle for analyzing political bias in news articles.</abstract>
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%0 Conference Proceedings
%T Predicting Political Orientation in News with Latent Discourse Structure to Improve Bias Understanding
%A Devatine, Nicolas
%A Muller, Philippe
%A Braud, Chloé
%S Proceedings of the 3rd Workshop on Computational Approaches to Discourse
%D 2022
%8 October
%I International Conference on Computational Linguistics
%C Gyeongju, Republic of Korea and Online
%F devatine-etal-2022-predicting
%X With the growing number of information sources, the problem of media bias becomes worrying for a democratic society. This paper explores the task of predicting the political orientation of news articles, with a goal of analyzing how bias is expressed. We demonstrate that integrating rhetorical dimensions via latent structures over sub-sentential discourse units allows for large improvements, with a +7.4 points difference between the base LSTM model and its discourse-based version, and +3 points improvement over the previous BERT-based state-of-the-art model. We also argue that this gives a new relevant handle for analyzing political bias in news articles.
%U https://aclanthology.org/2022.codi-1.10
%P 77-85

Markdown (Informal)

[Predicting Political Orientation in News with Latent Discourse Structure to Improve Bias Understanding](https://aclanthology.org/2022.codi-1.10) (Devatine et al., CODI 2022)

• Predicting Political Orientation in News with Latent Discourse Structure to Improve Bias Understanding (Devatine et al., CODI 2022)

ACL

• Nicolas Devatine, Philippe Muller, and Chloé Braud. 2022. Predicting Political Orientation in News with Latent Discourse Structure to Improve Bias Understanding. In Proceedings of the 3rd Workshop on Computational Approaches to Discourse, pages 77–85, Gyeongju, Republic of Korea and Online. International Conference on Computational Linguistics.