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.

中文翻译：

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建模、分析和理解社交媒体平台内的用户交互和各种属性

我们如何基于评论后关系有效地建模、分析和理解社交媒体平台内的用户交互和各种属性？在本研究中，我们提出了一种新颖的基于图的方法，根据评论后关系对社交媒体平台内的用户交互进行建模和分析。我们根据社交媒体数据构建用户交互图并对其进行分析，以深入了解社区动态、用户行为和内容偏好。我们的调查显示，虽然 56.05% 的活跃用户在社区内联系紧密，但只有 0.8% 的活跃用户对其动态做出了显着贡献。此外，我们观察到社区活动的时间变化，某些时期的参与度较高。此外，我们的研究结果强调了用户活动和受欢迎程度之间的相关性，表明更活跃的用户通常更受欢迎。除此之外，还观察到对积极和信息丰富的内容的偏好，其中 82.41% 的用户更喜欢积极和信息丰富的内容。总的来说，我们的研究为理解和管理在线社区提供了一个全面的框架，利用基于图形的技术来获得对用户行为和社区动态的宝贵见解。