Purpose
The purpose of this paper is to clarify the characteristics of growth users over a long time to strategically collect a large amount of specific users’ tweets. 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 along with real-life information from a large amount of tweets by assuming users as social sensors. To collect a large amount of tweets based on specific users for successful Twitter studies, the authors have to know the characteristics of users who are active over long periods of time.
Design/methodology/approach
The authors 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, the authors further classify Alive users into three types of Eraser, Slumber and Growth. The authors analyze the characteristic feature values observed in each user behavior and provide interesting findings with each status/type based on Gaussian mixture model clustering and point-wise mutual information.
Findings
From their sophisticated experimental evaluations, the authors found that active users more easily dropped out than inactive users, and users who engaged in reciprocal communications often became Growth type. Also, the authors found that active users and users who were not retweeted by other users often became Eraser type. The authors’ proposed methods effectively predicted Growth/Eraser-type users compared with the logistic regression model. From these results, the authors clarified the effectiveness of five feature values per active hour to detect intended Twitter user growth for strategically collecting a large amount of tweets.
Originality/value
The authors focus on user growth prediction. To appropriately estimate users who have potential for growth, they collect a large amount of users and explore their status and growth after three years. The research quantitatively clarifies the characteristics of growth users by clustering using robust feature values and provides interesting findings obtained by analysis. After that, the authors propose an effective prediction method for growth users and evaluate the effectiveness of their proposed method.
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