Measuring Dimensions of Self-Presentation in Twitter Bios and their Links to Misinformation Sharing
This work provides computational social scientists with improved tools for analyzing online self-presentation and offers insights into misinformation sharing on Twitter, though it is incremental in nature.
The authors tackled the problem of analyzing self-presentation in Twitter bios by proposing new embedding methods that outperform keyword-based approaches, and they applied these methods to find associations between bios and sharing of low-quality news URLs.
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 \hl{simple, effective, and theoretically motivated} approaches to embed bios in spaces that capture salient dimensions of social meaning, such as age and partisanship. We \hl{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\hl{, 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.