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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. 

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 studywork-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 intersectionsand transitions obtained in online self-presentation. 

Billions of dollars are being invested into developing medical artificial intelligence (AI) systems and yet public opinion of AI in the medical field seems to be mixed. Although high expectations for the future of medical AI do exist in the American public, anxiety and uncertainty about what it can do and how it works is widespread. Continuing evaluation of public opinion on AI in healthcare is necessary to ensure alignment between patient attitudes and the technologies adopted. We conducted a representative-sample survey (total N = 203) to measure the trust of the American public towards medical AI. Primarily, we contrasted preferences for AI and human professionals to be medical decision-makers. Additionally, we measured expectations for the impact and use of medical AI in the future. We present four noteworthy results: (1) The general public strongly prefers human medical professionals make medical decisions, while at the same time believing they are more likely to make culturally biased decisions than AI. (2) The general public is more comfortable with a human reading their medical records than an AI, both now and “100 years from now.” (3) The general public is nearly evenly split between those who would trust their own doctor to use AI and those who would not. (4) Respondents expect AI will improve medical treatment but more so in the distant future than immediately.