用机器学习提升线上政治讨论质量，让对话更理性有成效。
Machine Learning for Enhancing Deliberation in Online Political Discussions and Participatory Processes: A Survey
- 梳理线上政治讨论中的问题，提出机器学习应对方案。
- 总结现有带AI功能的平台工具及其实际效果。
- 适合关注数字民主与AI治理的研究者与实践者。
线上政治参与以公民讨论议题、交换意见的形式日益重要，而达成共识需要充分讨论与理性论辩，即“审议”。讨论质量高度依赖平台与流程设计。为帮助参与者与组织者，机器学习具有巨大潜力。本文通过文献综述，旨在：(i) 识别可通过人工智能解决的审议环节问题；(ii) 梳理已部署AI支持的工具与平台；(iii) 评估当前AI支持的有效性及现存挑战。研究覆盖多个关键任务，如观点识别、情绪分析、话语结构优化等，揭示技术在促进建设性交流方面的可行性与局限性。
原文摘要 · Abstract (English)
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.
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