Abstract
Personally expressed identity is who or what an individual themselves says they are, and it should be studied at scale. At scale means with data on millions of individuals, which is newly available and comes timestamped and geocoded. This work introduces a dataset for the study of identity at scale and describes the method for collecting and aggregating such data. Further, tools and theory for working with the data are presented. A demonstration analysis provides evidence that personal, individual development and changing cultural norms can be observed with these data and methods.
Highlights
Expressed identity should be studied at scale
Named “Annual Prevalence of American Twitter Users with specified Token in their Profile Bio 2015–2020,” the data provides a measure of the popularity of words chosen by Twitter users in the United States of America for inclusion in their profile biography
Longitudinal Online Profile Sampling (LOPS) stands for longitudinal online profile sampling
Summary
Expressed identity should be studied at scale. Personally expressed identity is who or what an individual themselves says they are. Studying identity with language is not new It was not new five decades ago, as Spitzer et al [2] lamented: “A perusal of Wylie’s The Self Concept [3] discloses the existence of no fewer than 100 instruments, only a small minority of which have seen repeated use. It seems that every student of the self-concept, either because of dissatisfaction with existing instruments or the choice of research problems, has contributed at least one additional device.” Many of these instruments were based on free-response prompts for self-descriptive language, sometimes collectively called “Who-am-I” instruments. The TST is comprised of twenty prompts of the same form: “I am _____.” This simple format is easy to administer and elicits rich language data. The current work has the advantages of studying personally expressed identity text relatively unobtrusively, at scale and longitudinally
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