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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.

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Currently, contents generated by SNS services are increasing exponentially, as the number of SNS users increase. The SNS is commonly used to post personal status and individual interests. Also, the SNS is applied in socialization, entertainment, product marketing, news sharing, and single person journalism. As SNS services became available on smart phones, the users of SNS services can generate and spread the social issues and controversies faster than the traditional media. The existing indexing methods for web contents have limitation in terms of real-time indexing for SNS contents, as they usually focus on diversity and accuracy of indexing. To overcome this problem, there are real-time indexing techniques based on RDBMSs. However, these techniques suffer from complex indexing procedures and reduced indexing targets. In this regard, we introduce the TK-Indexing method to improve the previous indexing techniques. Our method indexes the generation time of SNS contents and keywords by way of NoSQL to indexing SNS contents in real-time.

Self-adaptive software is software that adapts by itself to system requirements about the recognized problems without stopping the software cycle. In order to reduce the unnecessary adaptation in the system having the critical points, we propose proactive approach which can predict the future operation after a critical point. In this paper, we predict the future operation after a critical point using a hybrid model to deal with the characteristics of the observed data with the linear and non-linear pattern. The operation of the prediction method is determined on a timing decision indicator based on the prediction accuracy. The two main points of contributions of this paper are to reduce uncertainty about the future operation by predicting the situation after a critical point using hybrid model and to reduce unnecessary adaptation implementation by deciding a timing based on a timing decision indicator.