Media Exposure in Algorithmic Environments

Public health research focused on social media and online content as pathways of exposure and prevention has highlighted the increasing influence of digital media in everyday life for many people. Young people increasingly rely on digital media platforms for news, information, communication, and entertainment. Researchers have begun to consider social media as an environment within which young people may be exposed to unhealthful information and influences. Using standards of scientific rigor developed in the social and biological sciences, researchers have focused content analyses on samples of social media content (e.g., posts) using scale (large samples) or specificity (targeted samples) to validate results.

However, content sampling practices in this domain have often modeled digital search and content discovery in ways that resemble earlier search practices (such as the traditional library card catalog), with little consideration of the role of algorithmic systems in digital environments. As the scale and complexity of digitized data continues to expand beyond the bounds of traditional data processing techniques, researchers must rethink the methods and meanings we apply to collecting data and understanding media exposure in digital settings. Like YouTube and its parent company Google, many social media platforms (ex., Facebook) and search engines (ex., Bing) employ “smart” user-facing content algorithmic systems (or “algorithms” to use a shorthand in common use) for search and recommendation. While content discovery systems have access to a wealth of information about their users, users have comparatively little information about how these systems work.

Using our own experiences reviewing literature and sampling YouTube videos related to marijuana and tobacco co-use for a social media content analysis, we will (1) describe disconnects between media studies scholars’ concerns about algorithms and applications of social media content sampling in public health research; (2) highlight ways in which public health and Science and Technology Studies (STS) approaches to media can inform one another, and (3) advocate for further research focused on differential health impacts of algorithmic environments.