发表机构
Graduate School of Science and Technology, University of Tsukuba; Institute of Systems and Information Engineering, University of Tsukuba（筑波大学科学技术研究科教研究生院; 筑波大学系统信息工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究分析情绪分享平台Vent的大规模数据，发现用户发帖前时间线的情绪构成与后续情绪标签相关，且存在跨类别关联，高度对齐时间线情绪的用户在网络中位置相近，表明情绪表达受时间线情境和网络结构共同影响。
AI 中文摘要
社交媒体上情绪的分享与传染影响在线互动和用户的心理状态。本研究分析了 Vent（一个情绪分享社交媒体平台，用户在该平台上明确为自己的帖子标注情绪标签）上的情绪动态。利用大规模数据集，我们考察了用户发帖前时间线上的情绪如何与其后续情绪标签相关联，以及这种关联在不同用户之间如何变化。我们的分析揭示了三个关键发现。第一，用户后续的情绪标签与其发帖前时间线的情绪构成相关联，在所有分析类别中，帖子发布前同类情绪均过度呈现。第二，观察到若干跨类别关联；例如，惊讶在恐惧帖子之前过度呈现；喜爱和快乐在愤怒帖子之前过度呈现；喜爱在悲伤帖子之前过度呈现。第三，用户在与时间线情绪波动的对齐程度上存在差异，高度对齐的用户往往在网络中彼此位置相近。这些发现提供了大规模观察性证据，表明 Vent 上的情绪表达既与短期时间线情境相关，也与网络结构相关。
英文摘要
The sharing and contagion of emotions on social media influence online interactions and users' psychological states. This study analyzes emotional dynamics on Vent, an emotion-sharing social media platform where users explicitly assign emotional labels to their own posts. Using a large-scale dataset, we examine how emotions in users' pre-posting timelines are associated with their subsequent emotional labels and how such associations vary across users. Our analysis reveals three key findings. First, users' subsequent emotional labels are associated with the emotional composition of their pre-posting timelines, with same-category emotions being overrepresented before posts in all analyzed categories. Second, several cross-category associations are observed; for example, Surprise was overrepresented before Fear posts; Affection and Happiness were overrepresented before Anger posts; and Affection was overrepresented before Sadness posts. Third, users differ in their degree of alignment with timeline emotional fluctuations, and highly aligned users tend to be located close to one another in the network. These findings provide large-scale observational evidence that emotional expression on Vent is associated with both short-term timeline context and network structure.
