JMIR Publications

ABSTRACT

Background:

Social media has become a new source for obtaining real-world data (RWD) on adverse drug reactions (ADRs). Many studies have investigated the use of social media to detect early signals of ADRs. However, the trustworthiness of signals derived from social media is questionable. To confirm this, a confirmatory research study using positive control (e.g., new black box warnings, labeling changes, or withdrawals) is required.

Objective:

To evaluate the use of social media in detecting new black box warnings, labeling changes, or withdrawals in advance.

Methods:

This scoping review adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. A researcher searched PubMed and EMBASE in January of 2021. Original studies analyzing black box warnings, labeling changes, or withdrawals from social media were selected and the results of the studies were summarized.

Results:

A total of 14 articles were included in this scoping review. Most studies (8/14; 57.1%) collected data from a single source, and 10 of the 14 studies (71.4%) used specialized healthcare social networks and forums. The analytical methods used in the studies varied considerably. Three studies (21.4%) manually annotated posts, while five (35.7%) adopted machine learning algorithms. Nine studies (64.2%) concluded that social media could detect signals three months to nine years before regulatory authority action. Most (88.9%) of these studies were conducted on specialized healthcare social networks and forums. On the contrary, five studies (35.7%) showed modest or negative results. Of these, 50% used generic SNS, and 25% used generic SNS and specialized healthcare social networks and forums. The most recently published paper recommends not using social media for pharmacovigilance. Several challenges remain in using social media for pharmacovigilance regarding coverage, data quality, and analytic processing.

Conclusions:

Social media, along with conventional pharmacovigilance measures, can be used to detect signals associated with new black box warnings, labeling changes, or withdrawals. Several challenges remain; however, social media will be useful for signal detection of frequently mentioned drugs in specialized healthcare social networks and forums. Further studies are required to advance natural language processing and mine RWD on social media.