Computational and Causal Approaches on Social Media and Multimodal Sensing Data: Examining Wellbeing in Situated Contexts

Friday, April 2, 2021 - 11:00am

Time: Apr 2, 2021 11:00 AM Eastern Time (US and Canada)


A core aspect of our social lives is often embedded in the communities that we are situated in, such as our workplaces, neighborhoods, localities, and school/ college campuses. The inter-connectedness and inter-dependencies of our interactions, experiences, and concerns intertwine our situated context with our wellbeing. A better understanding of our wellbeing and psychosocial dynamics will help us devise strategies to address our wellbeing through proactive and tailored support strategies. However, existing methodologies to assess wellbeing suffer from limitations of scale and timeliness. Parallelly, given its ubiquity and widespread use, social media can be considered a “passive sensor” that can act as a complementary source of unobtrusive, real-time, and naturalistic data to infer wellbeing.

In this talk, Koustuv Saha, from Georgia Tech, will present computational and causal approaches for leveraging social media in concert with complementary multimodal data to examine wellbeing. He will show how theory-driven computational methods can be applied on unique social media and complementary multimodal data to capture attributes of human behavior and psychosocial dynamics in situated communities, particularly college campuses and workplaces. Further, he will dive deep into drawing meaning out of online-inferences about offline metrics. Finally, this talk will propel the vision towards human-centered technologies tailored to situations, demands, and needs, facilitating technology-supported remote functioning, evaluating the prospective utility of social platforms for wellbeing, and understanding the harms/benefits of computational and data-driven assessments.


Koustuv Saha is a doctoral candidate in Computer Science in the School of Interactive Computing at Georgia Tech. His research interest is in Social Computing and Computational Social Science. In his research, he adopts machine learning, natural language, and causal inference analysis to examine human behavior and wellbeing using social media and online data, along with complementary multimodal sensing data. His work has been published at several high prestige venues, including CHI, CSCW, ICWSM, IMWUT, JMIR, TBM, ACII, FAT*, PervasiveHealth, and WebSci, among others. He has been recognized as Foley Scholar, a recipient of the Foley Scholarship Award, GVU Center’s highest recognition for student excellence in research contributions to computing. He is a recipient of the Snap Research Fellowship, a finalist of the Symantec Graduate Fellowship, and his research has won the Outstanding Study Design Award at ICWSM 2019. His research has been covered at prestigious media outlets, including the New York Times, CBC Radio, NBC, 11Alive, the Hill, and the Commonwealth Times. During his Ph.D., he has had research internships at Snap Research, Microsoft Research, Max Planck Institute, and Fred Hutch Cancer Research. Earlier, he completed his B.Tech (Hons.) in Computer Science and Engineering from the Indian Institute of Technology (IIT) Kharagpur. He was also awarded the NTSE Scholarship by the Govt. of India, and he holds an overall industry research experience of five years. More about Koustuv can be found out at