Why Did They #Unfollow Me? Early Detection of Follower Loss on Twitter

Having more followers has become a norm in recent social media and micro-blogging communities. This battle has been taking shape from the early days of Twitter. Despite this strong competition for followers, many Twitter users are continuously losing their followers. This work addresses the problem of identifying the reasons behind the drop of followers of users in Twitter. As a first step, we extract various features by analyzing the content of the posts made by the Twitter users who lose followers consistently. We then leverage these features to early detect follower loss. We propose various models and yield an overall accuracy of 73% with high precision and recall. Our model outperforms baseline model by 19.67% (w.r.t accuracy), 33.8% (w.r.t precision) and 14.3% (w.r.t recall).Comment: 5 pages, 1 table, GROUP '1

Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction

Online Social Networks (OSNs) evolve through two pervasive behaviors: follow and unfollow, which respectively signify relationship creation and relationship dissolution. Researches on social network evolution mainly focus on the follow behavior, while the unfollow behavior has largely been ignored. Mining unfollow behavior is challenging because user's decision on unfollow is not only affected by the simple combination of user's attributes like informativeness and reciprocity, but also affected by the complex interaction among them. Meanwhile, prior datasets seldom contain sufficient records for inferring such complex interaction. To address these issues, we first construct a large-scale real-world Weibo dataset, which records detailed post content and relationship dynamics of 1.8 million Chinese users. Next, we define user's attributes as two categories: spatial attributes (e.g., social role of user) and temporal attributes (e.g., post content of user). Leveraging the constructed dataset, we systematically study how the interaction effects between user's spatial and temporal attributes contribute to the unfollow behavior. Afterwards, we propose a novel unified model with heterogeneous information (UMHI) for unfollow prediction. Specifically, our UMHI model: 1) captures user's spatial attributes through social network structure; 2) infers user's temporal attributes through user-posted content and unfollow history; and 3) models the interaction between spatial and temporal attributes by the nonlinear MLP layers. Comprehensive evaluations on the constructed dataset demonstrate that the proposed UMHI model outperforms baseline methods by 16.44% on average in terms of precision. In addition, factor analyses verify that both spatial attributes and temporal attributes are essential for mining unfollow behavior.Comment: 8 pages, 7 figures, Accepted by AAAI 202

#unfollow on Instagram Factors that have an impact on the decision to unfollow public figures

The social media platform Instagram allows users to subscribe to various people from their immediate circle of acquaintances or to follow public figures. Recent research has identified reasons concerning the discontinuance of social media use and the unfollowing behaviour on certain social media platforms. However, little is known about the unfollowing behaviour on Instagram and what causes users to unfollow public figures in particular. This study was the first trying to find out what factors influence users between the ages of 20 and 29 years of age to unfollow public figures. To this end, a total of nine qualitative guideline interviews were conducted with users recruited via Instagram. The interviews were analysed by means of an summary qualitative content analysis. Thereby, a total of eleven factors could be identified. The first factor relates to the negative feelings that arise when the content is received. The second and third factors relate to the public figure: behaviour and communication. The fourth, fifth and sixth factors relate to the frequency of posts, stories and the same content. The seventh, eighth, ninth, tenth and eleventh factors relate to content, but in different aspects: advertising, design, lack of identification, unfulfilled expectations and changes.The social media platform Instagram allows users to subscribe to various people from their immediate circle of acquaintances or to follow public figures. Recent research has identified reasons concerning the discontinuance of social media use and the unfollowing behaviour on certain social media platforms. However, little is known about the unfollowing behaviour on Instagram and what causes users to unfollow public figures in particular. This study was the first trying to find out what factors influence users between the ages of 20 and 29 years of age to unfollow public figures. To this end, a total of nine qualitative guideline interviews were conducted with users recruited via Instagram. The interviews were analysed by means of an summary qualitative content analysis. Thereby, a total of eleven factors could be identified. The first factor relates to the negative feelings that arise when the content is received. The second and third factors relate to the public figure: behaviour and communication. The fourth, fifth and sixth factors relate to the frequency of posts, stories and the same content. The seventh, eighth, ninth, tenth and eleventh factors relate to content, but in different aspects: advertising, design, lack of identification, unfulfilled expectations and changes