Self-reported biographical strings on social media profiles provide a powerful tool to study personal identity. We present Statewise, a dataset based on 50 million unique Twitter user profiles over a 12 year period identified to be in the United States. Users within this dataset can be accurately partitioned into 52 states/territories at each observation, allowing queries into state-specific language choices over time. We report on the major design decisions underlying Statewise, including the methodology behind the location detection system and measurements of user/state transitions across time. We demonstrate the power of Statewise to study the relative prevalences of different token groups, showing clear and consistent regional differences in language usage. We analyze emoji usage by comparing inclusion rates against external state-level statistics, finding that emoji inclusion shares a significant correlation with state unemployment and poverty rates. Finally, we use Gini coefficients as a measure of token usage inequality across all observed territories and demonstrate a clear stratification based on token content.

Occupational identity concerns the self-image of an individual’s affinities and socioeconomic class, and directs how a person should behave in certain ways. Understanding the establishment of occupational identity is important to study work-related behaviors. However, large-scale quantitative studies of occupational identity are difficult to perform due to its indirect observable nature. But profile biographies on social media contain concise yet rich descriptions about self- identity. Analysis of these self-descriptions provides powerful insights concerning how people see themselves and how they change over time. In this paper, we present and analyze a longitudinal corpus recording the self-authored public biographies of 51.18 million Twitter users as they evolve over a six-year period from 2015-2021. In particular, we investigate the social approval (e.g., job prestige and salary) effects in how people self-disclose occupational identities, quantifying over-represented occupations as well as the occupational transitions w.r.t. job prestige over time. We show that self-reported jobs and job transitions are biased toward more prestigious occupations. We also present an intriguing case study about how self-reported jobs changed amid COVID-19 and the subsequent "Great Resignation" trend with the latest full year data in 2022. These results demonstrate that social media biographies are a rich source of data for quantitative social science studies, allowing unobtrusive observation of the intersections and transitions obtained in online self-presentation.

Self-reported biographical strings on social media profiles provide a powerful tool to study self-identity. We present HINENI, a dataset of 420 million Twitter user profiles collected over a 12 year period, partitioned into 32 distinct national cohorts, which we believe is the largest publicly available data resource for identity research. We report on the major design decisions underlying HINENI, including a new notion of sampling (k-persistence) which spans the divide between traditional cross-sectional and longitudinal approaches. We demonstrate the power of HINENI to study the relative survival rate (half-life) of different tokens, and the use of emoji analysis across national cohorts to study the effects of gender, national, and sports identities.