JMIR Publications

ABSTRACT

Background:

Modifiable risky health behaviors such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits are major determinants for developing chronic health conditions. Social media platforms have become dominant means of communication in the digital era providing an opportunity for individuals to express themselves as well as share their health-related concerns with their peers and healthcare providers. Such peer interactions can be utilized as valuable data sources to better understand inter-and intra-personal psychosocial behavioral factors underlying risky health behaviors. They can further be leveraged to gain insights into the sociotechnical and information needs of individuals as they attempt to self-manage their health.

Objective:

The objective of this review was to summarize computational and quantitative techniques facilitating large-scale analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms.

Methods:

We performed a systematic review of the literature using PubMed and relevant keywords such as “social media”, “online health communities”, “machine learning”, “data mining”, etc. The reporting of the studies was directed by Preferred Reporting Items for Systematic Reviews and Meta-Analyses. One reviewer independently assessed the eligibility of the studies based on the inclusion and exclusion criteria and extracted the required information from the selected studies.

Results:

The initial search returned a total of 563 studies, and after careful analysis of titles, abstracts, and full texts, a total of 48 studies were included in this review. We extracted the following key characteristics from all the studies: social media platforms used for conducting the study, risky health behavior studied, study focus, informatics functions and tools used for data analysis and summary of the key findings. The most commonly used social media platform was Twitter followed by QuitNet and Reddit. The most commonly studied risky health behavior was nicotine dependence followed by drug or substance abuse and alcohol use. The most common analytical technique was text analysis using various supervised and unsupervised machine learning approaches. Few studies also utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed as reported in some studies.

Conclusions:

Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at individual-level and population-level.