Machine Learning for Enhancing Deliberation in Online Political Discussions and Participatory Processes: A Survey
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| author | Behrendt, Maike Wagner, Stefan Sylvius Weinmann, Carina Bormann, Marike Warne, Mira Harmeling, Stefan |
| author_facet | Behrendt, Maike Wagner, Stefan Sylvius Weinmann, Carina Bormann, Marike Warne, Mira Harmeling, Stefan |
| contents | Political online participation in the form of discussing political issues and exchanging opinions among citizens is gaining importance with more and more formats being held digitally. To come to a decision, a thorough discussion and consideration of opinions and a civil exchange of arguments, which is defined as the act of deliberation, is desirable. The quality of discussions and participation processes in terms of their deliberativeness highly depends on the design of platforms and processes. To facilitate online communication for both participants and initiators, machine learning methods offer a lot of potential. In this work we want to showcase which issues occur in political online discussions and how machine learning can be used to counteract these issues and enhance deliberation. We conduct a literature review to (i) identify tasks that could potentially be solved by artificial intelligence (AI) algorithms to enhance individual aspects of deliberation in political online discussions, (ii) provide an overview on existing tools and platforms that are equipped with AI support and (iii) assess how well AI support currently works and where challenges remain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02533 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning for Enhancing Deliberation in Online Political Discussions and Participatory Processes: A Survey Behrendt, Maike Wagner, Stefan Sylvius Weinmann, Carina Bormann, Marike Warne, Mira Harmeling, Stefan Computation and Language Human-Computer Interaction Political online participation in the form of discussing political issues and exchanging opinions among citizens is gaining importance with more and more formats being held digitally. To come to a decision, a thorough discussion and consideration of opinions and a civil exchange of arguments, which is defined as the act of deliberation, is desirable. The quality of discussions and participation processes in terms of their deliberativeness highly depends on the design of platforms and processes. To facilitate online communication for both participants and initiators, machine learning methods offer a lot of potential. In this work we want to showcase which issues occur in political online discussions and how machine learning can be used to counteract these issues and enhance deliberation. We conduct a literature review to (i) identify tasks that could potentially be solved by artificial intelligence (AI) algorithms to enhance individual aspects of deliberation in political online discussions, (ii) provide an overview on existing tools and platforms that are equipped with AI support and (iii) assess how well AI support currently works and where challenges remain. |
| title | Machine Learning for Enhancing Deliberation in Online Political Discussions and Participatory Processes: A Survey |
| topic | Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2506.02533 |