Special issue on Healthcare Social Analytics
IEEE Transactions on Network Science and Engineering
Impact Factor: 5.213


Aims and Scope
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.
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 special issue will be the use of social media data for building diagnostic, predictive and prescriptive analysis models for health research and applications such as analysing how social media can impact on people’s physical, mental and social health issue, and predicting users' health status and recommending solutions to prevent the risk of committing unfortunate actions such as suicide.
In this special issue, we solicit manuscripts from researchers and practitioners, both from academia and industry, from different disciplines such as computer science, big data mining, machine learning, social network analysis and other related areas to share their ideas and research achievements in order to deliver technology and solutions for healthcare social analytics.

Topics of Interest
We solicit original, unpublished and innovative research work on all aspects of the theme of this special issue. The topics of interest include but are not limited to:
Social media mining for automatic health monitoring and surveillance.
Predicting a user's health status on social media.
User behavior analysis and susceptibility prediction with regard to health-related data on social media.
Predictive models for early detection of trends in health-related issues on Social Media.
Early detection of disease outbreaks
Explainable AI for healthcare social media analytics.
Ethics, bias, and fairness in analysing social media for healthcare applications.
Analysing health-related misinformation on social media.
Prescriptive countermeasure methods against formation and circulation of health-related misinformation.
New datasets and evaluation methodologies to help healthcare social analytics.
Submission Guidelines
Papers submitted to this special issue for possible publication must be original and must not be under consideration for publication in any other journal or conference. Previously published or accepted conference papers must contain at least 30% new material to be considered for the special issue.
All papers are to be submitted through the journal editorial submission system. At the beginning of the submission process in the submission system, authors need to select "healthcare social analytics" as the article type. All manuscripts must be prepared according to the journal publication guidelines which can also be found on the website provided above. Papers will be evaluated following the journal's standard review process.

Important Dates
Submission Deadline: 1 November 2021
First Notification: 15 February 2022
Revisions Due: 1 April 2022
Issue of Publication: 2022

Guest Editors
Ebrahim Bagheri, Ryerson University
Diana Inkpen, University of Ottawa
Christopher C. Yang, Drexel University
Fattane Zarrinkalam, Thomson Reuters Labs
Daniel Dajun Zeng, Chinese Academy of Sciences