Occupations and Education in X Data : How representative is the data?

Abstract

"Valuable insights can be gained regarding jobs and professions across various sectors of society based on their inherent and acquired traits. Previous studies relied on methods such as action research, surveys, and questionnaires that are time-consuming and resource-intensive. This study examines vocational education and training data on Twitter. Although the data has been utilized in multiple studies, we will examine a vital research inquiry within computational social science: Is it plausible to employ Twitter/X data for analyzing vocational education and training in Germany or does the data display excessive bias? This investigation is infrequently explored since most researchers endeavor to discover representative samplings of larger subsets, and gauging representativeness against a ground truth can prove challenging. However, we will demonstrate that with research inquiry and statistical data, it is feasible to calculate a representative distance d, correction factors kappa, and an overall bias gamma. This provides a unique technique towards labor market research that makes novel data interoperable, which has not been considered in previous literature. Our approach is versatile and can be readily extended to other data." (Authors' abstract, BIBB-Doku)