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Social media platforms provide users with a profile description field, commonly known as a bio, where they can present themselves to the world. A growing literature shows that text in these bios can improve our understanding of online self-presentation and behavior, but existing work relies exclusively on keyword-based approaches to do so. We here propose and evaluate a suite of simple, effective, and theoretically motivated approaches to embed bios in spaces that capture salient dimensions of social meaning, such as age and partisanship. We evaluate our methods on four tasks, showing that the strongest one out-performs several practical baselines. We then show the utility of our method in helping understand associations between self-presentation and the sharing of URLs from low-quality news sites on Twitter, with a particular focus on explore the interactions between age and partisanship, and exploring the effects of self-presentations of religiosity. Our work provides new tools to help computational social scientists make use of information in bios, and provides new insights into how misinformation sharing may be perceived on Twitter.