Analyzing user migration in blockchain online social networks through network structure and discussion topics of communities on multilayer networks

Abstract

User migration, i.e. the movement of large sets of users from one online social platform to another one, is one of the main phenomena occurring in modern online social networks and even involves the most recent alternative paradigms of online social networks, such as blockchain online social networks (BOSNs). In these platforms, user migration mainly occurs through hard forks of the supporting blockchain, i.e. a split of the original blockchain and the creation of an alternative blockchain, to which users may decide to migrate. However, our understanding of user migration and its mechanisms is still limited, particularly regarding the role of densely connected user groups (communities) during migration and fork events. Are there differences between users who stay and those who decide to leave, in terms of network structure and discussion topics? In this work, we show, through network-based analysis centered on the identification of communities on multilayer networks and text mining that a) the “position” of a group within the network of social and economic interactions is connected to the likelihood of a group to migrate, i.e. marginal groups are more likely to leave; b) group network structure is also important, as users in densely connected groups interacting through monetary transactions are more likely to stay; c) users who leave are characterized by different discussion topics; and d) user groups interacting through monetary transactions show interest in migration-related content if they are going to leave. These findings highlight the importance of social and economic relationships between users during a user migration caused by fork events In general, in the larger context of online social media, it motivates the need to investigate user migration through a network-inspired approach based on groups and specific subgraphs while leveraging user-generated content, at the same time.