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The effects of politeness in shaping discourse in online debates.

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Title: The effects of politeness in shaping discourse in online debates.
Authors: Jeong, Allan1 allanjeong@gmail.com, Chiu, Ming Ming2
Source: Distance Education. May2025, Vol. 46 Issue 2, p253-271. 19p.
Subject Terms: *Critical thinking, *Interaction analysis in education, *Distance education, Telematics, Debate
Abstract: Computer-supported collaborative argumentation is an online activity that can engage students in deep discussion and analysis of complex problems. Given the potentially confrontational nature of argumentation, using polite language becomes a strategic approach to prevent breakdowns in group communication and nurture productive dialogues. This study aims to understand how politeness and argumentation moves influence subsequent conversation dynamics in online debates. Student postings in threaded discussions (from 20 online debates containing 2,008 messages posted by students across five semesters of a graduate-level course on distance education) were coded and scored on politeness and impoliteness using natural language processing software. The scored postings were examined to determine how impoliteness and politeness impact students' proclivity to engage in and produce more sustained argumentative exchanges to evaluate presented claims thoroughly. The findings show that impoliteness steadily increased by up to 84% as contentious discussion threads reached 7 levels deep, and impoliteness was significantly higher in frequency in postings presenting challenges. Correspondingly, the use of politeness increased steadily by up to 139% as discussion threads reached 6 levels deep, with the frequency of politeness nearly equal across postings presenting challenges, explanations, and evidence. [ABSTRACT FROM AUTHOR]
Copyright of Distance Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Distance+Education%22">Distance Education</searchLink>. May2025, Vol. 46 Issue 2, p253-271. 19p.
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  Data: Computer-supported collaborative argumentation is an online activity that can engage students in deep discussion and analysis of complex problems. Given the potentially confrontational nature of argumentation, using polite language becomes a strategic approach to prevent breakdowns in group communication and nurture productive dialogues. This study aims to understand how politeness and argumentation moves influence subsequent conversation dynamics in online debates. Student postings in threaded discussions (from 20 online debates containing 2,008 messages posted by students across five semesters of a graduate-level course on distance education) were coded and scored on politeness and impoliteness using natural language processing software. The scored postings were examined to determine how impoliteness and politeness impact students' proclivity to engage in and produce more sustained argumentative exchanges to evaluate presented claims thoroughly. The findings show that impoliteness steadily increased by up to 84% as contentious discussion threads reached 7 levels deep, and impoliteness was significantly higher in frequency in postings presenting challenges. Correspondingly, the use of politeness increased steadily by up to 139% as discussion threads reached 6 levels deep, with the frequency of politeness nearly equal across postings presenting challenges, explanations, and evidence. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Distance Education is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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