Machine Learning for Enhancing Deliberation in Online Political Discussions and Participatory Processes: A Survey

Maike Behrendt, Stefan Sylvius Wagner, Carina Weinmann, Marike Bormann, Mira Warne, Stefan Harmeling · Jun 3, 2025 · Citations: 0

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What to verify

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Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

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.

Abstract-only analysis — low confidence

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The available metadata is too thin to trust this as a primary source.
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The abstract does not clearly name benchmarks or metrics.

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Should You Rely On This Paper?

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Best use

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Use if you need

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Main weakness

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Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

Usefulness for eval research

Adjacent candidate

Extraction confidence 15%

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What We Could Verify

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