Analyzing Features of Passive Twitter’s Users to Estimate Passive Twitter-User’s Interests

Abstract

User modeling based on the contents of social network services has been developed to recommend information related to each user’s preference. Most of the previous studies have analyzed active users’ tweets and estimated their interests. Meanwhile, although more than a certain number of passive users do not tweet but only gather information, little research has been conducted on interest estimation due to the lack of clues for estimating their interests. These studies have achieved the estimation method using cues other than users’ tweets without understanding the behavior of passive Twitter users. Therefore, in this study, I analyzed the Twitter data with the user features used in the previous studies by using statistical methods to clarify the clue for extracting the interest of the passive user. To do so, a dataset including features of Twitter passive user and the active user was generated. The features of the passive user were clarified by statistical methods, such as Support Vector Machine, Principal Component Analysis, and Decision Tree Analysis. The results showed that it was possible to identify the passive user with an accuracy of 0.93 using features regarding user profiles, followers, and followed users. It was also found that most passive users had fewer than 8 followers and tended to be friendly connected to celebrities without self-disclosure. The results of this study identified types of Twitter passive users using the features. It contributes to the development of an interest estimation for the targeted types of a passive user.