In this study, we propose a novel graph-based approach to model, analyze and comprehend 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：本研究提出了一种新颖的基于图的方法，通过帖子-评论关系对社交媒体平台内的用户交互进行建模、分析和理解。我们从社交媒体数据构建用户交互图，并通过分析该图来深入理解社区动态、用户行为和内容偏好。研究发现，虽然56.05%的活跃用户在社区内具有强连接性，但仅有0.8%的用户对其动态发展产生显著贡献。此外，我们观察到社区活动存在时间性波动，特定时期会出现参与度激增的现象。研究结果还揭示了用户活跃度与受欢迎程度之间的相关性，表明活跃度更高的用户通常更受欢迎。同时，82.41%的用户表现出对积极性和信息性内容的偏好。总体而言，本研究通过运用基于图的技术，为理解和管理在线社区提供了一个综合框架，从而获得关于用户行为和社区动态的宝贵洞见。