21.15109/ARP/0XAKYA
Ilyés, Virág
Virág
Ilyés
https://orcid.org/0000-0003-2707-3042
HUN-REN Centre for Economic and Regional Studies
O’Szabo, Rebeka
Rebeka
O’Szabo
https://orcid.org/0000-0002-8802-5805
Corvinus University of Budapest
Császár, Olga
Olga
Császár
https://orcid.org/0000-0002-5011-0086
Corvinus University of Budapest
László, Lőrincz
Lőrincz
László
https://orcid.org/0000-0003-4528-0918
Corvinus University of Budapest
Replication Data for the paper ‘Authenticity and personal signals increase hiring chances in online labor markets: large-scale analysis of online impression management’
ARP
2026
Social Sciences
online labor markets
hiring outcomes
self-presentation
impression management
job application
skills
text analysis
Ilyés, Virág
Virág
Ilyés
HUN-REN Centre for Economic and Regional Studies
Ilyés, Virág
Virág
Ilyés
HUN-REN Centre for Economic and Regional Studies
2026-03-25
2026-03-30
21.15109/ARP/0XAKYA/TMIQ7N
21.15109/ARP/0XAKYA/WSUAMV
21.15109/ARP/0XAKYA/M7O6FG
21.15109/ARP/0XAKYA/R8HBPF
102070610
21222
27754
18780
text/csv
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
application/vnd.openxmlformats-officedocument.wordprocessingml.document
application/vnd.openxmlformats-officedocument.wordprocessingml.document
1.0
Custom terms specific to this dataset
The dataset was collected from Freelancer.com to examine how online impression management shapes hiring outcomes in gig-economy environments. 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.