在线辩论中意见选择的算法方法：一项比较研究
Algorithmic Approaches to Opinion Selection for Online Deliberation: A Comparative Study
AI总结：
本文提出了一种结合多样性和平衡代表性的算法，用于在线辩论中意见选择，以优化民主标准的权衡。
摘要：
在辩论过程中，调解员和促进者通常需要选择一小部分具有代表性的意见，以用于生成易于理解的报告，供相关方参考。在在线辩论平台上，算法选择越来越多地被用来自动化这一过程。然而，这种自动化并非没有后果。例如，强制执行寻求共识的算法策略可能意味着忽视或压低冲突偏好，这可能导致压制少数声音并减少内容多样性。更一般而言，在现有的各种选择策略（例如共识、多样性）中，仍然不清楚每种方法如何影响期望的民主标准，如比例代表性。为了解决这一差距，我们在此背景下对几种算法方法进行了基准测试。我们还借鉴社会选择理论，提出了一种新的算法，该算法在选择策略中结合了多样性和平衡的代表性概念。我们发现，虽然没有任何一种策略在所有民主需求上都占主导地位，但我们的受社会选择启发的选择规则在比例代表性和多样性之间实现了最强的权衡。
英文摘要：
During deliberation processes, mediators and facilitators typically need to select a small and representative set of opinions later used to produce digestible reports for stakeholders. In online deliberation platforms, algorithmic selection is increasingly used to automate this process. However, such automation is not without consequences. For instance, enforcing consensus-seeking algorithmic strategies can imply ignoring or flattening conflicting preferences, which may lead to erasing minority voices and reducing content diversity. More generally, across the variety of existing selection strategies (e.g., consensus, diversity), it remains unclear how each approach influences desired democratic criteria such as proportional representation. To address this gap, we benchmark several algorithmic approaches in this context. We also build on social choice theory to propose a novel algorithm that incorporates both diversity and a balanced notion of representation in the selection strategy. We find empirically that while no single strategy dominates across all democratic desiderata, our social-choice-inspired selection rule achieves the strongest trade-off between proportional representation and diversity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
