Who's Blocking Whom? Candidate Generation and Block Prediction on Bluesky

Abstract

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.