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Part 1: Document Description
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Citation |
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Title: |
Replication Data for the paper ‘Authenticity and personal signals increase hiring chances in online labor markets: large-scale analysis of online impression management’ |
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Identification Number: |
hdl:21.15109/ARP/0XAKYA |
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Distributor: |
ARP |
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Date of Distribution: |
2026-03-30 |
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Version: |
1 |
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Bibliographic Citation: |
Ilyés, Virág; O’Szabo, Rebeka; Császár, Olga; László, Lőrincz, 2026, "Replication Data for the paper ‘Authenticity and personal signals increase hiring chances in online labor markets: large-scale analysis of online impression management’", https://hdl.handle.net/21.15109/ARP/0XAKYA, ARP, V1 |
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Citation |
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Title: |
Replication Data for the paper ‘Authenticity and personal signals increase hiring chances in online labor markets: large-scale analysis of online impression management’ |
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Identification Number: |
hdl:21.15109/ARP/0XAKYA |
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Authoring Entity: |
Ilyés, Virág (HUN-REN Centre for Economic and Regional Studies) |
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O’Szabo, Rebeka (Corvinus University of Budapest) |
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Császár, Olga (Corvinus University of Budapest) |
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László, Lőrincz (Corvinus University of Budapest) |
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Producer: |
Ilyés, Virág |
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Distributor: |
ARP |
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Access Authority: |
Ilyés, Virág |
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Depositor: |
Ilyés, Virág |
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Date of Deposit: |
2026-03-25 |
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Holdings Information: |
https://hdl.handle.net/21.15109/ARP/0XAKYA |
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Study Scope |
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Keywords: |
Social Sciences, online labor markets, hiring outcomes, self-presentation, impression management, job application, skills, text analysis |
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Abstract: |
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. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Confidentiality Declaration: |
The dataset does not contain personal or confidential information to the best of the authors’ knowledge. |
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Special Permissions: |
No special permissions are required beyond compliance with the stated terms of use. |
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Restrictions: |
Use of the dataset is limited to research purposes. Commercial use is not permitted without prior permission from the data provider. |
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Citation Requirement: |
Users must cite the dataset in any publication or output using it. Proper citation should include the dataset title, authors, year, and repository. |
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Deposit Requirement: |
Users must not misrepresent the data or its source. |
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Conditions: |
By using this dataset, users agree to comply with these terms and conditions. |
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Disclaimer: |
The dataset is provided "as is" without warranty of any kind. The authors are not responsible for any errors or for any consequences arising from the use of the data. |
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Notes: |
This dataset is made available for research purposes only. Users are free to use, share, and adapt the data for non-commercial research activities, provided that proper citation is given. |
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Contact for Access: ilyes.virag@krtk.elte.hu ilyesvirag@gmail.com Terms of Access for Restricted Files: Access to the data is granted for research purposes upon request. Users must agree to cite the dataset appropriately. |
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Other Study Description Materials |
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Label: |
codebook.xlsx |
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Text: |
Variable-level documentation with names, descriptions, and variable types |
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Notes: |
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet |
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Label: |
custom_dictionaries.docx |
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Text: |
Detailed description of the custom dictionaries used for feature extraction |
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Notes: |
application/vnd.openxmlformats-officedocument.wordprocessingml.document |
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Label: |
data_full.csv |
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Text: |
Cleaned dataset used for the analysis, including all variables described in the codebook |
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Notes: |
text/csv |
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Label: |
documentation.docx |
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Text: |
Comprehensive documentation covering data collection, methodology, and dataset structure |
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Notes: |
application/vnd.openxmlformats-officedocument.wordprocessingml.document |