Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/91739
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | en_US |
dc.creator | Zeng, JC | en_US |
dc.creator | Li, J | en_US |
dc.creator | He, YL | en_US |
dc.creator | Gao, CY | en_US |
dc.creator | Lyu, MR | en_US |
dc.creator | King, R | en_US |
dc.date.accessioned | 2021-12-01T02:43:15Z | - |
dc.date.available | 2021-12-01T02:43:15Z | - |
dc.identifier.isbn | 978-1-4503-7023-3 (eisbn) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/91739 | - |
dc.language.iso | en | en_US |
dc.publisher | International World Wide Web Conference Committee | en_US |
dc.rights | This paper is published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution. | en_US |
dc.rights | © 2020 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Jichuan Zeng, Jing Li, Yulan He, Cuiyun Gao, Michael R. Lyu, and Irwin King. 2020. What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process. In Proceedings of The Web Conference 2020 (WWW ’20), April 20–24, 2020, Taipei, Taiwan. ACM, New York, NY, USA, 12 pages is available at https://doi.org/10.1145/3366423.3380223 | en_US |
dc.subject | Social media | en_US |
dc.subject | Argumentation mining | en_US |
dc.subject | Topic modeling | en_US |
dc.subject | Discourse modeling | en_US |
dc.subject | Dynamic data processing | en_US |
dc.title | What changed your mind : the roles of dynamic topics and discourse in argumentation process | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 1502 | en_US |
dc.identifier.epage | 1513 | en_US |
dc.identifier.doi | 10.1145/3366423.3380223 | en_US |
dcterms.abstract | In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society. Despite of the increasing attention to characterize human arguments, most progress made so far focus on the debate outcome, largely ignoring the dynamic patterns in argumentation processes. This paper presents a study that automatically analyzes the key factors in argument persuasiveness, beyond simply predicting who will persuade whom. Specifically, we propose a novel neural model that is able to dynamically track the changes of latent topics and discourse in argumentative conversations, allowing the investigation of their roles in influencing the outcomes of persuasion. Extensive experiments have been conducted on argumentative conversations on both social media and supreme court. The results show that our model outperforms state-of-the-art models in identifying persuasive arguments via explicitly exploring dynamic factors of topic and discourse. We further analyze the effects of topics and discourse on persuasiveness, and find that they are both useful - topics provide concrete evidence while superior discourse styles may bias participants, especially in social media arguments. In addition, we draw some findings from our empirical results, which will help people better engage in future persuasive conversations. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In The Web Conference 2020 : proceedings of the World Wide Web Conference WWW 2020 : Taipei 2020 : April 20-24, 2020, Taipei, Taiwan, p. 1502-1513 | en_US |
dcterms.issued | 2020-04-20 | - |
dc.identifier.isi | WOS:000626273301051 | - |
dc.identifier.scopus | 2-s2.0-85086598924 | - |
dc.relation.ispartofbook | The Web Conference 2020 : proceedings of the World Wide Web Conference WWW 2020 : Taipei 2020 : April 20-24, 2020, Taipei, Taiwan | en_US |
dc.relation.conference | Web Conference (WWW) | en_US |
dc.description.validate | 202112 bcwh | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Others | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | The work described in this paper is supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (No. CUHK 14210717 of the General Research Fund and No. CUHK 2410021 of the Research Impact Fund R5034-18). Jing Li is supported by the Hong Kong Polytechnic University Internal Fund (1-BE2W). YH is partly funded by the EPSRC grant EP/T017112/1. | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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3366423.3380223.pdf | 1.44 MB | Adobe PDF | View/Open |
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