My research develops novel computational approaches to model and examine the role of social media in improving our health and well-being.
As social media technologies are adopted more pervasively, the line between our online and offline lives is disappearing slowly but steadily. Content shared on these platforms often revolves around day-to-day happenings and experiences in our personal lives, and in our physical and social environments. As such, social media provides a means to capture attributes relevant to our thinking, mood, communication, activities, socialization, and psychological states. How can we employ this rich repository of information in addressing outstanding challenges relating to personal and societal well-being?
Well-being concerns like depression and mood disorders can have wide-reaching effects on people’s daily activities, education, employment, occupational functioning, and relationships. They are a leading cause of disability worldwide. According to the National Institutes of Mental Health, one in four adults, or 61.5 million Americans, are reported to experience such a challenge in a given year. However currently these well-being concerns are primarily assessed through self-report based mechanisms, e.g., an unstructured, open-ended approach, with limited training in evidence-based assessment methods. These methods require retrospective recollection of somewhat subjective well-being facts, and are therefore vulnerable to patients’ memory bias. Time and budgetary constraints often limit practicing psychiatrists from conducting more thorough and frequent di- agnostic evaluations. Moreover, the social stigma associated with mental health precludes people from seeking the care they need. Consequently, the need for pro-active detection of mental illness is emphasized, including alternative mechanisms to extend timely support to vulnerable individuals.
Many well-being challenges are known to be characterized by latent processes that include negative perspectives, self- focused attention, loss of self-worth and self-esteem, and social disengagement. Today, with social media, many of these latent processes, such as one’s affective, behavioral, and cognitive reactions can be observed in real-time, longitudinally, and unob- trusively. At the same time, individuals with well-being challenges are increasingly appropriating social media platforms to engage in candid disclosures of various well-being challenges. This has led to the emergence of many online communities that aim to provide scaffolding to individuals at risk. Taken together, can we employ social media to pro-actively detect one’s risk to well-being challenges, as well as to understand how they can facilitate support for improving one’s well-being? My research program outlines a vision to accomplish this goal.
Towards this goal, my research program adopts a highly interdisciplinary and collaborative approach, that carefully bal- ances methodological contributions with practical impact. Specifically, I combine the power of large-scale data analytics and machine learning with insights and theories from the social and behavioral science field, such as psychology, sociology, psychi- atry, and public health. I evaluate our developed computational approaches employing diverse quantitative and qualitative human-centered computing methods, deriving conclusions grounded in human behavior.
For further information on our specific projects and publications, please check out the Publications page.