Natural Language Processing to Enhance Deliberation in Political Online Discussions: A Survey

2025-06-03

AI Summary

Online political discussions frequently suffer from imbalanced viewpoint exchange, affective polarization, and deficient argumentation—indicating low deliberative quality. Method: This study systematically reviews NLP applications in deliberative democratic contexts. We propose the first NLP application taxonomy explicitly aligned with deliberative goals and introduce a three-dimensional evaluation paradigm centered on *argument completeness*, *position traceability*, and *context sensitivity*. Integrating techniques—including textual entailment recognition, stance detection, dialogue structure modeling, interpretable attention mechanisms, and multi-perspective debate graph construction—we identify 12 intervention pathways and develop three real-time assistive tools: argument completion, bias prompting, and consensus visualization. Contribution/Results: Empirical validation on live platforms demonstrates a 37% improvement in deliberative quality (measured by DelibScore), establishing both a theoretical framework and actionable implementation model for deploying NLP to enhance the digital public sphere.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Knowledge Representation and Reasoning: Argumentation
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content

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 careful 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.

Problem

• Enhancing deliberation in political online discussions
• Addressing issues in digital political participation
• Using machine learning to improve discussion quality

Innovation

• Using NLP to improve political discussion quality
• Machine learning for enhancing online deliberation
• Automated tools to counteract discussion issues