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A large amount of social data is being generated every day, as the Internet becomes more pervasive and mobile devices more ubiquitous. Accordingly, Internet users often experience difficulty finding the content they want, resulting in the popularity of personalized services that provide user-customized content. Intimacy between users of social network services can be utilized as a foundational technology for personalized services. In this paper, an intimacy measurement method for social networking services based on common neighbour similarity is proposed. The proposed method uses the link relationship between users for intimacy measurements and can be applied to general users. Further, it promotes easy data collection using publicly available data. To evaluate the proposed intimacy measurement method experimentally, a significant amount of user data was collected from Twitter. In addition, various statistical datasets were presented, and regression analyses conducted on graphs extracted from user data were collected to interpret the meaning of the intimacy index measured using the proposed method with existing social networking services.

Twitter is a microblogging website, which has different characteristics from any other social networking service (SNS) in that it has one-directional relationships between users with short posts of less than 140 characters. These characteristics make Twitter not only a social network but also a news media. In addition, Twitter posts have been used and analyzed in various fields such as marketing, prediction of presidential elections, and requirement analysis. With an increase in Twitter usage, we need a more effective method to analyze Twitter content. In this paper, we propose a method for content analysis based on the influence of Twitter content. For measuring Twitter influence, we use the number of followers of the content author, retweet count, and currency of time. We perform experiments to compare the proposed method, frequency, numerical statistics, user influence, and sentiment score. The results show that the proposed method is slightly better than the other methods. In addition, we discuss Twitter characteristics and a method for an effective analysis of Twitter content.