Who are growth users?: analyzing and predicting intended Twitter user growth

Abstract

Twitter reflects events and trends in users' real lives because many of them post tweets related to their experiences. Many studies have succeeded in detecting events such as earthquakes and influenza epidemics, along with real-life information from a large amount of tweets, by assuming users as social sensors. On the other hand, inactive users who don't engage in posting activity, are increasing according as time progresses. To collect a large amount of tweets based on specific users for successful Twitter studies, we have to know the characteristics of users who are active over long periods of time. In this paper, we clarify the characteristics of growth users over a long time to strategically collect a large amount of specific users' tweets. We explore the status of users who were active in 2012, and classify users into three statuses of Dead, Lock, and Alive. Based on the differences between the numbers of tweets in 2012 and 2016, we further classify alive users into three types of Eraser, Slumber, and Growth. We analyze the characteristic feature values observed in each user behavior and provide interesting findings with each status/type based on GMM clustering and point-wise mutual information. Finally, we propose a growth user prediction method by a simple formula consisting of feature values and evaluate the effectiveness. We found that active users more easily dropped out than inactive users, and users who engaged in reciprocal communications by replies and retweets often became Growth type.