A recent surge of users migrating from Twitter to alternative platforms, such as Mastodon, raised questions regarding what migration patterns are, how different platforms impact user behaviors, and how migrated users settle in the migration process. In this study, we elaborate on how we investigate these questions by collecting data over 10,000 users who migrated from Twitter to Mastodon within the first ten weeks following the ownership change of Twitter. Our research is structured in three primary steps. First, we develop algorithms to extract and analyze migration patterns. Second, by leveraging behavioral analysis, we examine the distinct architectures of Twitter and Mastodon to learn how user behaviors correspond with the characteristics of each platform. Last, we determine how particular behavioral factors influence users to stay on Mastodon. We share our findings of user migration, insights, and lessons learned from the user behavior study.
University of Zurich（苏黎世大学）；London School of Economics and Political Science（伦敦政治经济学院）；Paris School of Economics（巴黎经济学校）；Imperial College London（帝国理工学院）
Community Detection for Heterogeneous Multiple Social Networks
School of Computer Science and Technology, China University of Mining and Technology（中国矿业大学计算机科学与技术学院）；College of Computer and Information Engineering, Nanjing Tech University（南京工业大学计算机与信息工程学院）；School of Cyber Science and Engineering, Southeast University（东南大学网络空间科学与工程学院）；Purple Mountain Laboratories（紫金山实验室）
