Sensing Subjective Well-being from Social Media

Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users' social media data with SWB labels, we train machine learning models that are able to "sense" individual SWB from users' social media. Our model, which attains the state-by-art prediction accuracy, can then be used to identify SWB of large population of social media users in time with very low cost.Comment: 12 pages, 1 figures, 2 tables, 10th International Conference, AMT 2014, Warsaw, Poland, August 11-14, 2014. Proceeding

Understanding and Measuring Psychological Stress using Social Media

A body of literature has demonstrated that users' mental health conditions, such as depression and anxiety, can be predicted from their social media language. There is still a gap in the scientific understanding of how psychological stress is expressed on social media. Stress is one of the primary underlying causes and correlates of chronic physical illnesses and mental health conditions. In this paper, we explore the language of psychological stress with a dataset of 601 social media users, who answered the Perceived Stress Scale questionnaire and also consented to share their Facebook and Twitter data. Firstly, we find that stressed users post about exhaustion, losing control, increased self-focus and physical pain as compared to posts about breakfast, family-time, and travel by users who are not stressed. Secondly, we find that Facebook language is more predictive of stress than Twitter language. Thirdly, we demonstrate how the language based models thus developed can be adapted and be scaled to measure county-level trends. Since county-level language is easily available on Twitter using the Streaming API, we explore multiple domain adaptation algorithms to adapt user-level Facebook models to Twitter language. We find that domain-adapted and scaled social media-based measurements of stress outperform sociodemographic variables (age, gender, race, education, and income), against ground-truth survey-based stress measurements, both at the user- and the county-level in the U.S. Twitter language that scores higher in stress is also predictive of poorer health, less access to facilities and lower socioeconomic status in counties. We conclude with a discussion of the implications of using social media as a new tool for monitoring stress levels of both individuals and counties.Comment: Accepted for publication in the proceedings of ICWSM 201

Virtual worlds are authentic sites for learning

This chapter considers how ‘meaningful learning’ can be understood in the context of knowledge-age skills. Through a study conducted in Second Life™, it investigates whether terms such as ‘authentic’, ‘active’ and ‘collaborative’ can be applied to activities undertaken in virtual worlds. It examines the knowledge-age skills employed in virtual worlds, relating these skills to the characteristics of the learning environment. Finally, it asks whether the distinction between meaningful and non-meaningful learning environments is more important for the development of knowledge-age skills than the distinction between formal and informal situations or between staff-run and student-run situations

The game transfer phenomena scale: an instrument for investigating the nonvolitional effects of video game playing

A variety of instruments have been developed to assess different dimensions of playing videogames and its effects on cognitions, affect, and behaviors. The present study examined the psychometric properties of the Game Transfer Phenomena Scale (GTPS) that assesses non-volitional phenomena experienced after playing videogames (i.e., altered perceptions, automatic mental processes, and involuntary behaviors). A total of 1,736 gamers participated in an online survey used as the basis for the analysis. Confirmatory factor analysis (CFA) was performed to confirm the factorial structure of the GTPS. The five-factor structure using the 20 indicators based on the analysis of gamers’ self-reports fitted the data well. Population cross-validity was also achieved and the positive associations between the session length and overall scores indicate the GTPS warranted criterion-related validity. Although the understanding of GTP is still in its infancy, the GTPS appears to be a valid and reliable instrument for assessing non-volitional gaming-related phenomena. The GTPS can be used for understanding the phenomenology of post-effects of playing videogames