  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 using real data shows that the proposed algorithm provides
representative outcomes in practice.
