Online communication via avatars provides a richer online social experience than text communication. This reinforces the importance of online social support. Online social support is effective for people who lack social resources because of the anonymity of online communities. We aimed to understand online social support via avatars and their social relationships to provide better social support to avatar users. Therefore, we administered a questionnaire to three avatar communication service users (Second Life, ZEPETO, and Pigg Party) and three text communication service users (Facebook, X, and Instagram) (N=8,947). There was no duplication of users for each service. By comparing avatar and text communication users, we examined the amount of online social support, stability of online relationships, and the relationships between online social support and offline social resources (e.g., offline social support). We observed that avatar communication service users received more online social support, had more stable relationships, and had fewer offline social resources than text communication service users. However, the positive association between online and offline social support for avatar communication users was more substantial than for text communication users. These findings highlight the significance of realistic online communication experiences through avatars, including nonverbal and real-time interactions with co-presence. The findings also highlighted avatar communication service users' problems in the physical world, such as the lack of offline social resources. This study suggests that enhancing online social support through avatars can address these issues. This could help resolve social resource problems, both online and offline in future metaverse societies.

Early detection of bipolar disorder (BD) is crucial for its ultimate control and prevention. We aim to detect people with BD and their manic/hypomanic episodes through their abnormal behaviors of following peers in Massively Multiplayer Online Game (MMOG) logs. The total participants consisted of 198,605 users of Pigg Party: a popular MMOG in Japan. Their behaviors, including gacha (an online capsule toy game) and purchase in MMOG, were recorded in milli seconds for one month. Of the total participants, 291 responded to the mood disorder questionnaire. Among the questionnaire respondents, 20 were judged as BD group and the other 271 as non-BD group. According to the anomaly scores of the behaviors of following peers, 13,650 of the total participants were estimated as BD group and the other 184,955 participants as non-BD group. Among the total participants, the BD group had significantly more gacha and purchase behaviors than the non-BD group. Further, among this BD group, these behaviors were significantly more prevalent especially during manic/hypomanic episodes. Our findings indicate that behaviors of following peers in MMOG logs is potentially useful for detecting BD and manic/hypomanic episodes.

We aim to investigate the influence of television on the web; if the influence is strong, a viral effect may be expected. In this paper, we focus on the Internet TV station and on Wikipedia use as exploratory behavior on the web. We analyzed the influence of Internet TV station on Wikipedia page views. Our aim is to clarify the characteristics of page views as related to Internet TV station in order to index outward impact and develop a prediction model. The results indicate that there is a correlation between TV viewership and page views. Moreover we find that the time lag between TV and web gradually reduce as broadcasts begin after 9:00; after 23:00, page views tend to be maximized during the broadcast itself. We also differentiate between page views on PC and on mobile and find that PC pages tend to be accessed more during the daytime. In addition, we consider the number of broadcasts per program, and observe that viewership tends to stabilize as the number of broadcasts increases but that page views tend to decrease.

Personalization, including both self-selected and pre-selected, is inevitable when tremendous amounts of media content are available. Since personalization, which is believed to encourage people to consume fewer diverse contents, can lead to fragmentation and polarization in society, exposing people to more diverse contents is important. In this paper, we investigate using web TV data what behavioral and environmental features influence users to consume more diverse contents. We tackle this problem by building a classification model that predicts whether users will consume contents that are comprised of more diverse sources and features. We measure the change of the consumed content diversity of users with the method in our previous work and design six features based on our basic research and previous studies. The result of our experiment shows that our model predicts with high accuracy the change of the consumed content diversity of users, which means that the increase of the diversity of the consumed content of users is explained well with our six features. Our analysis of the model shows that consuming contents across multiple genres and inadvertent exposure to various contents positively affect users and encourage them broaden their horizons. The former has especially strong influence. In addition, we demonstrate that active-seeking behaviors of users and the amount of content in their most interested areas negatively affect the increase of their consumed content diversity. Our findings in this study are expected to be applied to web media to moderate the effect of selective exposure and reduce fragmentation and polarization in society.

Computational social science is an emerging interdisciplinary field which uses computational techniques to model, simulate and analyze human behavior and social phenomena, typically by leveraging the power of big data. Some researchers in this field use the records of actions taken by players in online games, meaning that they obtain a large amount of human behavioral data without explicitly conducting experiments with human subjects. This study investigates the mechanism of a social phenomenon called behavioral bursts which occur in a social networking game with over 7 million players. We define a behavioral burst as an increase in average points obtained by players that goes over the estimated value by a threshold. The results show that the burst patterns are classified into two classes depending on their cause. One is exogenous burst, caused by external factors (e.g. end of events), and the other is endogenous burst, caused by player interaction. We then investigate the change in the behavior of players between before and during the bursts, and find that only a small number of players are involved in them in case of endogenous bursts. Furthermore, by focusing on the action sequences of players obtaining many points, we find that they communicate with other players in a modest number of times, and in the case of endogenous bursts they tend to exchange information with a fixed player.

People frequently change their music listening behaviors to fit their mood and particular situation. Such changes can be interpreted at various scales, e.g., the genres, artists, or specific tracks. Users of music streaming services expect to discover new music. However, discovering new music is not always a pleasant experience. Herein, we show that small changes in listening trends are good for users of such services. In contrast, large changes are bad. We modeled user listening trends using a hidden Markov model, which was applied hierarchically to analyze the user trends at multiple scales. We evaluated the changes in user listening trends to find good changes. Additionally, we analyzed the relationships between user listening trends and user lifestyle.

We propose a new analytical method to classify web user behavior based on such latent states of users as intention, interest, or motivation. First, we put the clickstream data of many users into a Hidden Markov Model in which the number of hidden states is large enough to build a state transition network. Since the variable hidden states represent different latent states of users, the movement on the state transition network can represent user behavior. Second, we divide each piece of clickstream data into sessions, which we classify using network movement as feature values. These cluster labels represent the latent states of users during their stay in the Web service. In this paper, we applied our method to the data of a social network game named Girl Friend BETA, which is an online game that is mainly provided on social networking services. We observed the following hidden states that represent the variable latent states of users, such as enthusiasm for the main contents of the service, playing basic content, and daily routines that are well observed by visiting the service: e.g. receiving login bonuses. Also, we classified the sessions by the latent states of users, such as light user sessions, low motivation sessions, and sessions in which users seem addicted to the main contents.