Replication Data for the paper ‘Authenticity and personal signals increase hiring chances in online labor markets: large-scale analysis of online impression management’

It combines detailed information from project pages and freelancer profile pages to construct a linked project–applicant–level dataset. The data include rich information on project requirements, applicant bids, skills, reputation, and demographic proxies, as well as extensive textual features derived from application messages and personal introductions.

These textual measures capture multiple dimensions of self-presentation, including structure, readability, formality, politeness strategies, sentiment, emotional tone, and persuasive language, alongside indicators of authenticity such as text uniqueness and introduction style. The primary goal of data collection is to provide large-scale empirical evidence on whether and how online self-presentation—beyond traditional “hard” signals like skills and experience—affects hiring decisions, and to identify which impression management strategies are rewarded or penalized across different types of work in online labor markets.