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        <title>Exploring the Role of Argument Structure in Online Debate Persuasion</title>
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        <namePart type="given">Jialu</namePart>
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    <abstract>Online debate forums provide users a platform to express their opinions on controversial topics while being exposed to opinions from diverse set of viewpoints. Existing work in Natural Language Processing (NLP) has shown that linguistic features extracted from the debate text and features encoding the characteristics of the audience are both critical in persuasion studies. In this paper, we aim to further investigate the role of discourse structure of the arguments from online debates in their persuasiveness. In particular, we use the factor graph model to obtain features for the argument structure of debates from an online debating platform and incorporate these features to an LSTM-based model to predict the debater that makes the most convincing arguments. We find that incorporating argument structure features play an essential role in achieving the best predictive performance in assessing the persuasiveness of the arguments on online debates.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.716</identifier>
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%0 Conference Proceedings
%T Exploring the Role of Argument Structure in Online Debate Persuasion
%A Li, Jialu
%A Durmus, Esin
%A Cardie, Claire
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-exploring-role
%X Online debate forums provide users a platform to express their opinions on controversial topics while being exposed to opinions from diverse set of viewpoints. Existing work in Natural Language Processing (NLP) has shown that linguistic features extracted from the debate text and features encoding the characteristics of the audience are both critical in persuasion studies. In this paper, we aim to further investigate the role of discourse structure of the arguments from online debates in their persuasiveness. In particular, we use the factor graph model to obtain features for the argument structure of debates from an online debating platform and incorporate these features to an LSTM-based model to predict the debater that makes the most convincing arguments. We find that incorporating argument structure features play an essential role in achieving the best predictive performance in assessing the persuasiveness of the arguments on online debates.
%R 10.18653/v1/2020.emnlp-main.716
%U https://aclanthology.org/2020.emnlp-main.716
%U https://doi.org/10.18653/v1/2020.emnlp-main.716
%P 8905-8912

Markdown (Informal)

[Exploring the Role of Argument Structure in Online Debate Persuasion](https://aclanthology.org/2020.emnlp-main.716) (Li et al., EMNLP 2020)

• Exploring the Role of Argument Structure in Online Debate Persuasion (Li et al., EMNLP 2020)

ACL

• Jialu Li, Esin Durmus, and Claire Cardie. 2020. Exploring the Role of Argument Structure in Online Debate Persuasion. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8905–8912, Online. Association for Computational Linguistics.