We used quantitative semantics to find clusters of words in LinkedIn users' self-descriptions to an employer or a friend. Some of these clusters discriminated between worker and friend conditions (e.g., flexible vs. caring) and between LinkedIn users with high and low education (e.g., analytical vs. messy).
Keywords: LinkedIn; identity; latent semantic analysis; personality; self-description.
© 2018 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.