谁在屏蔽谁？Bluesky上的候选生成与屏蔽预测

中文摘要 AI 辅助

屏蔽是一种被广泛使用的工具，帮助人们管理社交平台上不受欢迎的互动。我们研究Bluesky平台上的屏蔽事件预测问题：给定近期互动、网络、活动和内容信号，预测某个用户是否会屏蔽特定账户。利用超过300万次屏蔽事件和超过2.6亿次用户互动，我们研究了定向屏蔽预测问题的多种形式，这些形式在考虑的可能目标以及负例选择方式上存在差异。我们观察到，只有约5%的用户屏蔽事件之前存在近期直接互动；将可能目标扩展到包含通过共同邻居连接的账户后，一旦排除高账户作为中间节点，该比例提升至约22%。在候选包含的条件下，Hits@1范围为61.3%至72.4%，而随机基线为16.7%。不过，预测性能及模型使用的信号高度依赖于候选和对比示例的构建方式。尽管我们的目标是对用户行为进行实证分析，而非提出可部署系统，但这些发现对面向用户的审核工具具有相关性，这类工具可帮助用户识别可能希望避开或屏蔽的账户；在此类工具中，候选生成将是重要的设计与评估选择，而非仅为预处理步骤。

英文摘要

Blocking is a widely used tool that helps people manage unwanted interactions on social platforms. We study the problem of predicting block events on Bluesky: whether a given user will block a particular account, given recent interaction, network, activity, and content signals. Using more than three million block events and over 260 million user interactions, we examine several formulations of the directed block prediction problem, differing in which possible targets are considered and how negative examples are selected. We observe that only about 5% of user blocks are preceded by a recent direct interaction. Expanding the set of possible targets to include accounts connected through a common neighbor raises the share to about 22%, once high-degree accounts are excluded as intermediaries. Conditional on candidate inclusion, Hits@1 ranges from 61.3% to 72.4%, compared with a random baseline of 16.7%. However, predictive performance and the signals used by the models depend strongly on how candidates and comparison examples are constructed. Although our goal is to empirically analyze user behavior rather than to propose a deployable system, these findings are relevant to user-facing moderation tools that might help users identify accounts they may wish to avoid or block. In such tools, candidate generation would be a substantive design and evaluation choice rather than merely a preprocessing step.

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9 pages, 4 figures