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In recent years the number of individuals struggling with mental illness has increased, and traditional mental health services are now considered insufficient under the current circumstances which has prompted researchers to develop new approaches for mental healthcare. Social media usage is growing, and it is been utilized to help provide additional insight on mental health by using the information shared by individuals, as well as data taken from their social media activity. While this approach may provide a unique and effective perspective for mental health services, it is critical that privacy risks and protections are considered in the process. Social media services collect, process, and stores a substantial amount of information about its users and how that information is shared as well as what type of predictions are made may pose serious privacy concerns. This study aims to understand how privacy is addressed and emphasized during the process of using social media data for mental healthcare by constructing a systematic review on previous scholarly papers related to the topic. Solove's taxonomy of privacy is used to evaluate these publications privacy considerations and to demonstrate the privacy risks that may arise when social media data is used for mental health. [ABSTRACT FROM AUTHOR]

Beyond the easy and equal access to massive online contents, will digital reading bring more social welfare values for disadvantaged groups? Take left‐behind children (LBC) in rural China as example, whose mental health issue has widely aroused public concern. Lacking of sufficient parental supervision and educational resources leads to insufficient development of psychological resilience and makes them vulnerable to mental health problems. As psychological resilience is a critical protective factor for maintaining mental health, in this paper, we wonder whether digital reading could perform as an alternative way with easy accessibility and numerous resources to supplement LBC's resilience development. We conducted a field questionnaire study on LBC (N = 217) and investigated the predictive effect of digital reading on psychological resilience. After controlling sociodemographic variables and common resilience protective factors, hierarchical regression results demonstrated that digital reading predicted an additional 4.3% of the variation in resilience above the control variables, indicating digital reading a positive promoter of LBC's psychological resilience. Moreover, by systematically exploring the fine‐grained digital reading variables, we also found intrinsic motivation to read and recreational digital reading (e.g., reading comics and communicating online) to be the two most stronger predictors of psychological resilience. [ABSTRACT FROM AUTHOR]

This work‐in‐progress paper reports preliminary analysis of an online survey of 224 university students (undergraduate or graduate), examining their help‐seeking behavior, information needs, information‐seeking behavior, and information‐seeking satisfaction when experiencing stress, anxiety, or depression symptoms. Information on the Internet was the most commonly used source of help and information. While most demonstrated at least mild mental illness symptoms, students were inclined to seek information around self‐help/care rather than around professional help. This study extends information behavior research to the mental health context, and our future analyses will assess how students' information behavior relates to their psychological and sociodemographic factors. [ABSTRACT FROM AUTHOR]

Although using mobile phone apps to track and monitor mental health is increasingly popular, we know little about how and why people with mental health diagnoses use such technologies, nor do we know of concerns they may express about these apps. Further, automated assessments of mental health status, or digital phenotyping, are also on the rise; how people with mental health conditions feel about such technologies is also an underexplored area of research. This paper presents an exploratory interview study with 12 people that begins to address these gaps. We focus specifically on how participants, who all have been diagnosed mental health conditions, perceive tech companies' involvement with existing mental health apps and mental health digital phenotyping. We find that participants satisfice in order to interact with existing mood tracking apps, and that they are wary of digital phenotyping for mental health diagnostics. Participants raise concerns related to profit motives, distrust, and fatalism in both cases, and they recommend regulations that may be put in place to keep tech companies in check. Though participants describe regulatory policies that may mitigate their concerns, we question whether regulations can truly foster sustainable interactions with mental health apps operated by technology companies. [ABSTRACT FROM AUTHOR]

In recent decades, there has been an alarming increase in the number of university and college students struggling with intensifying psychological distress, which has become a mental health crisis on campuses. To address this issue, this study proposes social VR as a potential platform to promote social interaction and improve mental well‐ being for college students. In this study, 68 students explored a variety of social VR applications in a classroom setting. Results showed that a) virtual space, audio, avatar, communication types, and activities were key contributing features that facilitated social interaction among college students in social VR and have the potential to enhance mental wellness, and b) the anonymous nature, communicative cues, and designated private areas provided by social VR platforms were effective in facilitating self‐disclosure, indicating social VR's potential in delivering mental health services such as individual and group counseling and therapy. This study provides evidence that social VR can enhance social interaction and communication while serving as a platform for professional mental health care, a venue that has yet to be explored in previous studies. [ABSTRACT FROM AUTHOR]

An accurate and timely assessment of mental health of individuals is an important building block toward healthy well‐functioning societies. Traditional methods for assessing mental health and wellbeing are surveys administered by health‐care professionals. Such surveys are labor intensive, costly, and typically intermittent. With the growth in mobile sensing, health informatics, and data science, this work argues a case for using mobile phone meta‐data and data analytics to automatically infer an individual's mental health. Specifically, based on a clinically utilized health measure as ground truth (MHI‐5) and ten‐week phone usage metadata corpus for 45 participants we report that automated machine learning algorithms utilizing phone based features can achieve reasonable accuracy (around 80%) at automatically classifying the level of mental health of an individual. Results could pave way for cheaper, automated, health assessments with timely escalations to mental health professionals and interventions when required. [ABSTRACT FROM AUTHOR]