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Many Twitter users post tweets that are related to their particular interests. Users can also collect information by following other users. One approach clarifies user interests by tagging labels based on the users. A user tagging method is important to discover candidate users with similar interests. This paper aims to propose a new user tagging method using the posting time series data of the number of tweets.

Our hypothesis focuses on the relationship between a user’s interests and the posting times of tweets: as users have interests, they will post more tweets at the time when events occur compared with general times. The authors assume that hashtags are labeled tags to users and observe their occurrence counts in each timestamp. The authors extract burst timestamps using Kleinberg’s burst enumeration algorithm and estimate the burst levels. The authors manage the burst levels as term frequency in documents and calculate the score using typical methods such as cosine similarity, Naïve Bayes and term frequency (TF) in a document and inversed document frequency (IDF; TF-IDF).

From the sophisticated experimental evaluations, the authors demonstrate the high efficiency of the tagging method. Naïve Bayes and cosine similarity are particular suitable for the user tagging and tag score calculation tasks, respectively. Some users, whose hashtags were appropriately estimated by our methods, experienced higher the maximum value of the number of tweets than other users.

Many approaches estimate user interest based on the terms in tweets and apply such graph theory as following networks. The authors propose a new estimation method that uses the time series data of the number of tweets. The merits to estimating user interest using the time series data do not depend on language and can decrease the calculation costs compared with the above-mentioned approaches because the number of features is fewer.

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.

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.

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.

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.

Japan is home to a relatively conservative and group-oriented culture in which social expectations can exert powerful pressure to conform to traditional patterns of behaviour. This includes gender norms, which have long been based around the common stereotypes of men as breadwinners and women as housewives. Social liberalisation and economic change in the late 20th century saw these patterns change as more women entered the workforce and, despite Japan's dismal standing in global equality rankings, began to make inroads into some positions of political and corporate leadership. Yet, the way in which women are treated by men is shaped not only by female gender norms but also by the social factors that determine male patterns of behaviour. This chapter considers how Japan's male gender norms, particularly the focus on man as economic labourers rather than active members of the family unit, have damaged many men's ability to connect, on an emotional level, with the women in their lives. It looks at the issue of misogyny; what is known as the Lolita Complex; the growing trend of herbivore men; and the concept of Ikumen, men who are active within the family. While some of these patterns of behaviour can be harmful – for women on the individual level, and for Japan as a whole, on the social level – there are some trends which suggest that gender norms in Japan can be directed in a manner which will allow for much healthier emotional relationships to develop between the genders in a manner that will help build a society that is more cognisant of and attentive to the needs of women.