6th International Workshop on Mining Actionable Insights from Social Networks
Special Edition on
Healthcare Social Analytics
Keynotes
Talk: NLP Applications in Mental Health
Abstract. With the ever-increasing usage of social media to either explicitly seek help or to simply share thoughts and feelings, we, in the computational disciplines, have the opportunity to utilize such data for building datasets, models, and doing analysis. I will share our collaborative work done at the Information Retrieval Lab at Georgetown University on detecting mental health concerns in social media posts. The first application is on a dedicated mental health forum where the users who register to share and communicate their thoughts and feelings are suffering from some sort of mental distress (sadness, depression, potential of self-harm, ….). The task is to triage the severity of users’ posts to detect early the potential of self-harm as well as to evaluate the impact of forum activities and conversations on the users during a period of time. In the second type of platform, i.e., non-dedicated, I focus on the question of whether we can detect if a user is suffering from any one or more of nine mental health conditions, only using the *general language* of the user; that is, the posts are not in mental health [sub]forums nor have any mental health related words. To address this question, we had to construct large scale datasets; I will explain how we have identified the diagnosed users, and how carefully selected the controls. Further I will show the results of several baselines to detect the conditions. Identifying whether a given mental health condition of a user is a recent condition, similarly, whether the user currently suffers from a condition, are important questions yet challenging tasks as our efforts have shown us. Detecting mental health conditions based on a relatively smaller number of posts generally is not promising; hence it is important to mitigate our approaches so that such low resource users do not go undetected. Finally, to address information reduction for a faster read and processing of users’ posts by moderators/counselors, I will provide our effort to summarize the posts to their short forms.
Bio. Nazli Goharian is Clinical Professor of Computer Science and Associate Director of the Information Retrieval Lab at Georgetown University, which she co-founded in 2010. She joined the Illinois Institute of Technology (IIT) from industry in 2000. Her research and doctoral student mentorship span the domains of information retrieval, text mining, and natural language processing. Specifically, her interest lies in humane-computing applications such as medical/health domain. Joint with her doctoral students, she received an EMNLP 2017 Best Long Paper Award and COLING 2018 Honorable Mention both for papers on mental health and social media. For contributions to undergraduate and graduate curriculum development and teaching excellence, she was recognized with the IIT Julia Beveridge Award for faculty (university-wide female faculty of the year) in 2009, the College of Science and Letters Dean’s Excellence Award in Teaching in 2005, and in 2002, 2003, and 2007, the Computer Science Department Teacher of the Year Award. She served as Senior/Area Chairs at ACL 2018, ACL 2019, ACL 2020 and ACL 2021. She is co-chair of SIGIR Women in Information retrieval (WIR) since 2019, focusing on gender pay inequity and women leadership..
Talk: Employing Social Media to Improve Mental Health: Pitfalls, Lessons Learned, and the Next Frontier
Abstract. Social media data is being increasingly used to computationally learn about and infer the mental health states of individuals and populations. Despite being touted as a powerful means to shape interventions and impact mental health recovery, little do we understand about the theoretical, domain, and psychometric validity of this novel information source, or its underlying biases, when appropriated to augment conventionally gathered data, such as surveys and verbal self-reports. This talk presents a critical analytic perspective on the pitfalls of social media signals of mental health, especially when they are derived from “proxy” diagnostic indicators, often removed from the real-world context in which they are likely to be used. Then, to overcome these pitfalls, this talk presents results from two case studies, where machine learning algorithms to glean mental health insights from social media were developed in a context-sensitive and human-centered way, in collaboration with domain experts and stakeholders. The first of these case studies, a collaboration with a health provider, focuses on the individual-perspective, and reveals the ability and implications of using social media data of consented schizophrenia patients to forecast relapse and support clinical decision-making. Scaling up to populations, in collaboration with a federal organization and towards influencing public health policy, the second case study seeks to forecast nationwide rates of suicide fatalities using social media signals, in conjunction with health services data. The talk concludes with discussions of the path forward, emphasizing the need for a collaborative, multi-disciplinary research agenda while realizing the potential of social media data and machine learning in mental health -- one that incorporates methodological rigor, ethics, and accountability, all at once.
Bio. Munmun De Choudhury is an Associate Professor of Interactive Computing at Georgia Tech. Dr. De Choudhury is best known for laying the foundation of a new line of research that develops computational techniques towards understanding and improving mental health outcomes, through ethical analysis of social media data. To do this work, she adopts a highly interdisciplinary approach, combining social computing, machine learning, and natural language analysis with insights and theories from the social, behavioral, and health sciences. Dr. De Choudhury has been recognized with the 2021 ACM-W Rising Star Award, 2019 Complex Systems Society – Junior Scientific Award, numerous best paper and honorable mention awards from the ACM and AAAI, and features and coverage in popular press like the New York Times, the NPR, and the BBC. Dr. De Choudhury currently serves on the Board of Directors of the International Society for Computational Social Science and on the Steering Committee of the International Conference on Web and Social Media, the leading conference on interdisciplinary studies of social media. Earlier, Dr. De Choudhury was a faculty associate with the Berkman Klein Center for Internet and Society at Harvard, a postdoc at Microsoft Research, and obtained her PhD in Computer Science from Arizona State University.