JMIR Publications

ABSTRACT

Background:

Social media has changed the communication landscape, exposing individuals to an ever-growing amount of information while also allowing them to create and share content. While individuals have always had some concerns about vaccination, social media has amplified these concerns and facilitated their spread globally. Multiple studies have been conducted to monitor vaccination discussions on social media. However, there is currently insufficient evidence on the best methods to perform social media monitoring.

Objective:

This study aims to identify the methods most commonly used for monitoring different social media platforms around vaccination, their effectiveness and limitations.

Methods:

A systematic scoping review was conducted by applying a comprehensive search strategy to multiple databases in December 2018. The articles’ titles, abstracts and full texts were screened by two reviewers using inclusion and exclusion criteria. After data extraction, a descriptive analysis was performed to summarize the methods used to monitor and analyze social media, including data extraction tools, ethical considerations, search strategies, periods monitored, geo-localization of content, and sentiments, content and reach analyzes.

Results:

This review identified 86 articles on social media monitoring of vaccination, most of them published after 2015. While 35 out of the 86 studies used manual browser search tools to collect data from social media, this was more time-consuming and only allowed the analysis of smaller samples compared to social media application program interfaces (APIs) or automated monitoring tools. Although simple search strategies were considered less precise, only 10 out of the 86 studies used comprehensive lists of keywords (e.g., with hashtags or words related to specific events or concerns). Partly due to privacy settings, geo-localization of data was extremely difficult to obtain, limiting the possibility of conducting country-specific analyzes. Finally, while 20 out of the 86 studies performed trend- or content-analyzes, most analyzed sentiments towards vaccination (70% of studies, 60/86). Automated sentiment analyzes, conducted using leverage or supervised machine learning or automated software, were faster and provided stronger and more accurate results than manual coding. Most studies focused on negative (n=33) and positive (n=31) sentiments towards vaccination, and may have failed to capture the nuances and complexity of emotions around vaccination. Finally, 49 out of the 86 studies determined the reach of social media by looking at numbers of followers and engagement with particular posts (e.g., retweets, shares, likes, etc.).

Conclusions:

Social media monitoring still constitutes a new means to research and understanding public sentiments around vaccination. A wide range of methods are currently used by researchers, sometimes without sufficient ethical considerations. Future research should focus on evaluating these methods to offer more evidence and support the development of social media monitoring as a valuable research design.