Description
Social networks are increasingly seen as a promising source of data for mental health research. The resulting applications are manifold, from improving diagnostics and monitoring patient activity, to developing digital interventions and targeted advertising of mental health services. This panel will deliver on some key learning objectives in the area: (1) Understand the ethics and governance frameworks; (2) Understand recommended technology for collecting data from popular social networks (Twitter, Facebook, Reddit); (3) Discuss approaches to complex concept definitions needed for working in the mental health domain; and (4) Learn about current state-of-the-art NLP and machine learning techniques for processing social network data, their capabilities, and what the future may hold for this field.
Learning Objective: (1) Understand the ethics and governance frameworks for using social media data for mental health research;
(2) Understand recommended technology for collecting data from popular social networks (Twitter, Facebook, Reddit);
(3) Discuss approaches to complex concept definitions needed for working in the mental health domain;
(4) Learn about current state-of-the-art natural language processing and machine learning techniques for processing social network data, their capabilities, and what the future may hold for this field.
Authors:
Vasa Curcin (Presenter)
King's College London
Elizabeth Ford (Presenter)
Brighton and Sussex Medical School
Jyotishman Pathak (Presenter)
Cornell University
Lamiece Hassan (Presenter)
The University of Manchaster
Presentation Materials: