BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky Social

Decentralized social media platforms like Bluesky Social (Bluesky) have made it possible to publicly disclose some user behaviors with millisecond-level precision. Embracing Bluesky's principles of open-source and open-data, we present the first collection of the temporal dynamics of user-driven social interactions. BlueTempNet integrates multiple types of networks into a single multi-network, including user-to-user interactions (following and blocking users) and user-to-community interactions (creating and joining communities). Communities are user-formed groups in custom Feeds, where users subscribe to posts aligned with their interests. Following Bluesky's public data policy, we collect existing Bluesky Feeds, including the users who liked and generated these Feeds, and provide tools to gather users' social interactions within a date range. This data-collection strategy captures past user behaviors and supports the future data collection of user behavior.

去中心化的社交媒体平台，如Bluesky Social（Bluesky），使得以毫秒级的精确度公开披露某些用户行为成为可能。秉持Bluesky开源与开放数据的原则，我们首次呈现了由用户驱动的社交互动的时间动态集合。BlueTempNet将多种类型的网络整合为一个单一的多网络结构，其中包括用户间互动（关注与屏蔽用户）以及用户与社区间的互动（创建与加入社区）。社区是在自定义Feeds中由用户构成的群体，用户在此订阅与其兴趣相符的帖子。遵循Bluesky的公开数据政策，我们收集了现有的Bluesky Feeds，包括喜欢和生成这些Feeds的用户，并提供了工具以收集用户在特定日期范围内的社交互动。这一数据收集策略捕捉了用户过去的行为了解，并支持未来对用户行为的持续数据收集。