Leveraging Consensus Effect to Optimize Ranking in Online Discussion Boards
研究发现在线讨论中评论的共识水平影响用户参与，提出一种考虑共识的动态排序算法，在教育场景实验中比现有方法更有效提升参与度。
Problem definition: Online discussion platforms (often referred to as discussion boards) are designed for facilitating remote discussions between users. To stimulate engagement (e.g., participation in the discussion), these platforms offer arriving users a ranked list of existing discussion comments. In this paper, we formalize the level of consensus in the discussion and study its impact on engagement and how it could be leveraged by ranking algorithms to increase engagement along the discussion path. Methodology/results: We collaborate with a leading online discussion board for education settings. Analyzing data from online discussions, we identify the level of consensus in the discussion as a new engagement driver. The presence of the consensus effect suggests that ranking algorithms should consider not only comments that would induce engagement in the present period but also ones that would maximize future engagement by managing the desired level of consensus. Based on this insight, we propose a new dynamic model for ranking optimization and a class of intuitive algorithms that, among other factors, account for the level of consensus when prescribing rankings that maximize engagement using a limited lookahead. In a randomized experiment consisting of eight discussion groups in an education setting, our proposed algorithm outperformed the approach used in current practice (that does not actively manage the level of consensus). Managerial implications: Our study proposes consensus as an essential factor in user engagement and in the design of user interface in online platforms and demonstrates the performance improvement that is achievable by leveraging it in the design of ranking algorithms in discussion boards. In doing so, our study suggests that online platforms may often benefit from rankings that build debate rather than an “echo chamber” of consensus. History: This paper has been accepted as part of the 2023 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0451 .