Aims and Scope

Context. With the emergence and growing popularity of social media such as blogging systems, wikis, social bookmarking, social networks and microblogging services, many users are extensively engaged in at least some of these applications to express their feelings and views about a wide variety of social topics as they happen in real time by commenting, tagging, joining, sharing, liking, and publishing posts. According to Statista, there were an estimated 2.65 billion people using social media in 2018, a number projected to increase to almost 3.1 billion in 2021 . This has resulted in an ocean of data which presents an interesting opportunity for performing data mining and knowledge discovery in many domains including healthcare. The recent highly impressive advances in machine learning and natural language processing present exciting opportunities for developing automatic methods for the collection, extraction, representation, analysis, and validation of social media data for health applications. These methods should be able to simultaneously address the unique challenges of processing social media data and timely discover meaningful patterns identifying emerging health threats.

Goal. Traditional research on healthcare social analytics mainly focuses on descriptive methods such as tracking health trends on social media and tracking infectious disease spread. The main distinguishing focus of this workshop will be the use of social media data for building diagnostic, predictive and prescriptive analysis models for health research and applications such as analyzing how social media can impact on people’s mental health issue, and predicting users' health status and recommending solutions to prevent the risk of committing unfortunate actions such as suicide.