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Communication has long been concerned with people’s media repertoires, yet little of this approach has extended to the combination of social media platforms that people use. Despite their considerable popularity, research has found that people do not select into the use of social network sites (SNSs) randomly, which has implications for both whose voices are represented on them and where messaging can reach diverse people. While prior work has considered self-selection into one SNS, in this article we ask: how are different SNSs linked by user base? Using national survey data about 1,512 US adults’ social media uses, we build networks between SNSs that connect SNS pairs by user base. We examine patterns by subgroups of users along the lines of age, gender, education, and Internet skills finding considerable variation in SNS associations by these variables. This has implications for big data analyses that depend on data from particular social media platforms. It also offers helpful lessons for how to reach different population segments when trying to communicate to diverse audiences. 

The COVID-19 global pandemic led governments, health agencies, and technology companies to work on solutions to minimize the spread of the disease. One such solution concerns contact-tracing apps whose utility is tied to widespread adoption. Using survey data collected a few weeks into lockdown measures in the United States, we explore Americans’ willingness to install a COVID-19 tracking app. Specifically, we evaluate how the distributor of such an app (e.g., government, health-protection agency, technology company) affects people’s willingness to adopt the tool. While we find that 67 percent of respondents are willing to install an app from at least one of the eight providers included, the factors that predict one’s willingness to adopt differ. Using Nissenbaum’s theory of privacy as contextual integrity, we explore differences in responses across distributors and discuss why some distributors may be viewed as less appropriate than others in the context of providing health-related apps during a global pandemic. We conclude the paper by providing policy recommendations for wide-scale data collection that minimizes the likelihood that such tools violate the norms of appropriate information flows.