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.

翻译：像Bluesky社交平台（Bluesky）这样的去中心化社交媒体平台，使得以毫秒级精度公开披露部分用户行为成为可能。秉承Bluesky的开源与开放数据原则，我们首次呈现了用户驱动的社交交互的时序动态数据集。BlueTempNet将多种类型的网络整合为一个统一的多网络，包括用户对用户交互（关注与屏蔽用户）以及用户对社区交互（创建与加入社区）。社区是用户在自定义Feeds中形成的群组，用户可订阅与其兴趣相符的帖子。遵循Bluesky的公共数据政策，我们收集了现有的Bluesky Feeds，包括喜欢和生成这些Feeds的用户，并提供了在指定日期范围内收集用户社交交互的工具。该数据收集策略不仅捕捉了历史用户行为，也为未来用户行为的数据收集提供了支持。