A core aspect of our lives involves interactions with the communities we are situated in. Understanding how we cope with psychological and cognitive demands is essential for individual and collective wellbeing. Psychosocial dynamics of individuals are typically assessed using surveys, which, though accurate in snapshots, suffer from recall bias, are reactive, and are difficult to scale. These limitations are surmountable by social and ubiquitous technologies. Our research leverages social media in concert with multimodal sensing data, which facilitates analyzing dense and longitudinal behavior at scale.

By adopting machine learning, natural language, and causal inference analysis techniques, we examine the behaviors and wellbeing of individuals and collectives, particularly in situated communities, such as college campuses and workplaces. Our work questions the underlying assumptions of data-driven inferences and the possible harms such inferences might lead to. This body of research is situated in an interdisciplinary and human-centered context and bears design and technological implications for sociotechnical systems and various stakeholders.

For instance, the following figure describes the different (and not necessarily independent) components of our research interests within the domain of digital wellbeing.