How can we effectively model, analyze, and comprehend user interactions and various attributes within a social media platform based on post-comment relationship? In this study, we propose a novel graph-based approach to model and analyze user interactions within a social media platform based on post-comment relationship. We construct a user interaction graph from social media data and analyze it to gain insights into community dynamics, user behavior, and content preferences. Our investigation reveals that while 56.05% of the active users are strongly connected within the community, only 0.8% of them significantly contribute to its dynamics. Moreover, we observe temporal variations in community activity, with certain periods experiencing heightened engagement. Additionally, our findings highlight a correlation between user activity and popularity showing that more active users are generally more popular. Alongside these, a preference for positive and informative content is also observed where 82.41% users preferred positive and informative content. Overall, our study provides a comprehensive framework for understanding and managing online communities, leveraging graph-based techniques to gain valuable insights into user behavior and community dynamics.

翻译：如何基于发帖-评论关系对社交媒体平台中的用户交互及多属性信息进行有效建模、分析与理解？本研究提出一种基于图的新方法，通过发帖-评论关系对社交媒体平台中的用户交互进行建模与分析。我们基于社交媒体数据构建用户交互图，通过分析该图深入理解社区动态、用户行为及内容偏好。研究发现：虽然56.05%的活跃用户与社区存在强连接，但仅0.8%的用户对社区动态有显著贡献。此外，我们观察到社区活动存在时间变化规律，某些时段活跃度显著提升。同时，研究结果揭示了用户活跃度与受欢迎程度的相关性——活跃度越高的用户通常越受欢迎。在内容偏好方面，82.41%的用户倾向于积极向上的信息性内容。总体而言，本研究为理解与管理在线社区提供了综合框架，通过图技术手段获取用户行为与社区动态的深层洞见。