Kim, S.J., Marsch, L.A., Hancock, J.T. & Das, A.K. (2017). Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data. J Med Internet Res, 19(10):e353.JMIR PDF
Background: Substance use–related communication for drug use promotion and its prevention is widely prevalent on social media. Social media big data involve naturally occurring communication phenomena that are observable through social media platforms, which can be used in computational or scalable solutions to generate data-driven inferences. Despite the promising potential to utilize social media big data to monitor and treat substance use problems, the characteristics, mechanisms, and outcomes of substance use–related communications on social media are largely unknown. Understanding these aspects can help researchers effectively leverage social media big data and platforms for observation and health communication outreach for people with substance use problems
Objective: The objective of this critical review was to determine how social media big data can be used to understand communication and behavioral patterns of problematic use of prescription drugs. We elaborate on theoretical applications, ethical challenges and methodological considerations when using social media big data for research on drug abuse and addiction. Based on a critical review process, we propose a typology with key initiatives to address the knowledge gap in the use of social media for research on prescription drug abuse and addiction
Methods: First, we provided a narrative summary of the literature on drug use–related communication on social media. We also examined ethical considerations in the research processes of (1) social media big data mining, (2) subgroup or follow-up investigation, and (3) dissemination of social media data-driven findings. To develop a critical review-based typology, we searched the PubMed database and the entire e-collection theme of “infodemiology and infoveillance” in the Journal of Medical Internet Research / JMIR Publications. Studies that met our inclusion criteria (eg, use of social media data concerning non-medical use of prescription drugs, data informatics-driven findings) were reviewed for knowledge synthesis. User characteristics, communication characteristics, mechanisms and predictors of such communications, and the psychological and behavioral outcomes of social media use for problematic drug use–related communications are the dimensions of our typology. In addition to ethical practices and considerations, we also reviewed the methodological and computational approaches used in each study to develop our typology
Results: We developed a typology to better understand non-medical, problematic use of prescription drugs through the lens of social media big data. Highly relevant studies that met our inclusion criteria were reviewed for knowledge synthesis. The characteristics of users who shared problematic substance use–related communications on social media were reported by general group terms, such as adolescents, Twitter users, and Instagram users. All reviewed studies examined the communication characteristics, such as linguistic properties, and social networks of problematic drug use–related communications on social media. The mechanisms and predictors of such social media communications were not directly examined or empirically identified in the reviewed studies. The psychological or behavioral consequence (eg, increased behavioral intention for mimicking risky health behaviors) of engaging with and being exposed to social media communications regarding problematic drug use was another area of research that has been understudied
Conclusions: We offer theoretical applications, ethical considerations, and empirical evidence within the scope of social media communication and prescription drug abuse and addiction. Our critical review suggests that social media big data can be a tremendous resource to understand, monitor and intervene on drug abuse and addiction problems.