  Social media users on sites like Twitter, Instagram, and Tiktok use the
profile description, or bio, field of user profiles to present themselves to
the world. In contrast to the ``offline'' world, where social context often
encourages us to adopt a single identity, the profile description is a
free-text field in which users are encouraged to present the self using
multiple, sometimes conflicting, social identities. While sociologists, social
psychologists, sociolinguists, and increasingly computational social
scientists, have developed a large and growing array of methods to estimate the
meaning of individual social identities, little work has attended to the ways
in which social meanings emerge from the collections of social identities
present in social media bios. The present work proposes and evaluate three
novel, identity-based methods to measure the social dimensions of meaning
expressed in Twitter bios. We show that these models outperform reasonable
baselines with respect to 1) predicting which sets of identities are more
likely to co-occur within a single biography and 2) quantifying perceptions of
entire social media biographies along salient dimensions of social meaning on
Twitter, in particular partisanship. We demonstrate the utility of our method
in a computational social science setting by using model outputs to better
understand how self presentation along dimensions of partisanship, religion,
age, and gender are related to the sharing of URLs on Twitter from low versus
high quality news sites.
