In conference peer review, reviewers are often asked to provide "bids" on each submitted paper that express their interest in reviewing that paper. A paper assignment algorithm then uses these bids (along with other data) to compute a high-quality assignment of reviewers to papers. However, this process has been exploited by malicious reviewers who strategically bid in order to unethically manipulate the paper assignment, crucially undermining the peer review process. For example, these reviewers may aim to get assigned to a friend's paper as part of a quid-pro-quo deal. A critical impediment towards creating and evaluating methods to mitigate this issue is the lack of any publicly-available data on malicious paper bidding. In this work, we collect and publicly release a novel dataset to fill this gap, collected from a mock conference activity where participants were instructed to bid either honestly or maliciously. We further provide a descriptive analysis of the bidding behavior, including our categorization of different strategies employed by participants. Finally, we evaluate the ability of each strategy to manipulate the assignment, and also evaluate the performance of some simple algorithms meant to detect malicious bidding. The performance of these detection algorithms can be taken as a baseline for future research on detecting malicious bidding.

翻译：在会议同行评审中，评审员通常需要为每篇提交的论文提供"竞标"，以表达其审阅意愿。论文分配算法随后利用这些竞标（结合其他数据）生成高质量的评审员-论文分配方案。然而，这一流程已被恶意评审员所利用——他们通过策略性竞标来不道德地操纵论文分配，从根本上破坏了同行评审机制。例如，这些评审员可能试图被分配至其朋友的论文，作为互惠交易的一部分。当前，制约恶意竞标缓解方法研究与评估的关键障碍在于缺乏公开可用的恶意论文竞标数据。本研究通过模拟会议活动收集并公开该领域首个新型数据集——参与者被要求按诚实或恶意两种模式进行竞标。我们进一步对竞标行为进行了描述性分析，包括对参与者采用的不同策略进行分类。最后，我们评估了每种策略对分配结果的操纵能力，并检验了若干简单检测算法的性能。这些检测算法的表现可作为未来恶意竞标检测研究的基准。