Platforms for online civic participation rely heavily on methods for condensing thousands of comments into a relevant handful, based on whether participants agree or disagree with them. These methods should guarantee fair representation of the participants, as their outcomes may affect the health of the conversation and inform impactful downstream decisions. To that end, we draw on the literature on approval-based committee elections. Our setting is novel in that the approval votes are incomplete since participants will typically not vote on all comments. We prove that this complication renders non-adaptive algorithms impractical in terms of the amount of information they must gather. Therefore, we develop an adaptive algorithm that uses information more efficiently by presenting incoming participants with statements that appear promising based on votes by previous participants. We prove that this method satisfies commonly used notions of fair representation, even when participants only vote on a small fraction of comments. Finally, an empirical evaluation on real data shows that the proposed algorithm provides representative outcomes in practice.

翻译：在线公民参与平台高度依赖于将数千条评论浓缩为相关若干条的方法，这些方法基于参与者是否同意或不同意这些评论。这些方法应确保参与者的公平代表性，因为其结果可能影响对话的健康性，并指导具有重大影响的下游决策。为此，我们借鉴了基于批准的委员会选举文献。我们的场景具有新颖性：由于参与者通常不会对所有评论进行投票，因此批准投票是不完全的。我们证明，这种复杂性使得非自适应算法在所需收集的信息量方面不可行。因此，我们开发了一种自适应算法，通过向新参与的参与者展示基于先前参与者投票而看似有希望的陈述，更高效地利用信息。我们证明，即使参与者只对一小部分评论进行投票，该方法也能满足常用的公平代表性概念。最后，基于真实数据的实证评估表明，所提出的算法在实践中能提供具有代表性的结果。