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Transgender people use social media for identity work, which takes place over time and across platforms. In this study, we interviewed 20 transgender social media users to examine transgender identity management across the social media ecosystem. We found that transgender social media users curate their social media experiences to fit their needs through creating accounts on different platforms, maintaining multiple accounts on individual platforms, and making active decisions about content they post, networks they are connected to, and content they interact with. In this way, transgender people's social media curation is not limited to their own identity presentations, but also involves curating the content they see from others and whom they include in their networks. Together, these two types of online curation enable transgender social media users to craft social media worlds that meet their social and self-presentational needs.

AbstractBig data is everywhere, both in and out of public policy. Though a rich data source, what is the impact of big data beyond the research community? We suggest such that invoking big data-related terms acts as a heuristic for assumed algorithmic quality. Such an assumption leads to greater trust in automation in public policy decision-making. We test this "big-data-effect" expectation using four tests including a conjoint experiment embedded in a recently fielded survey experiment. We find strong evidence that indeed, big data-related terms act as powerful signals of assumed quality where respondents consistently prefer algorithms with bigger data behind them, absent any mention of predictive accuracy or definitions of key terms (e.g. "training features"). As we expect this big-data-effect is likely not beholden to public policy, we encourage more research in this vein to deepen an understanding of the influence of big data on modern society.