Crowdsourcing Research: Research Area — Prolific Citations Library

Discover 23 peer-reviewed studies in Crowdsourcing Research (2022–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 23 peer-reviewed papers in the research area of Crowdsourcing Research in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (20 of 23)

The Daily Lives of Crowdsourced US Respondents: A Time Use Comparison of MTurk, Prolific, and ATUS

Authors: RG Rinderknecht, L Doan

Year: 2025

Published in: Sociological ..., 2025 - journals.sagepub.com

Institution: RAND

Research Area: Crowdsourcing Research Methods, Time Use Studies, Social Science

Discipline: Artificial Intelligence

Time use patterns of MTurk and Prolific respondents differ significantly from the general U.S. population (ATUS), including less housework and care work, more time at home and alone, even after accounting for demographic differences.

Methods: Time diaries were collected and analyzed for 136 MTurk and 156 Prolific respondents, then compared with 468 ATUS responses.

Key Findings: Daily time use patterns including work, housework, travel, leisure, and time spent alone or at home.

Citations: 6

Sample Size: 760
Unlocking Insights into Prolific: Research Implications, Participant Behavior and Motivations

Authors: B Aksoy, S Nevo

Year: 2025

Published in: Participant Behavior and Motivations (March 21 ..., 2025 - papers.ssrn.com

Institution: Rensselaer Polytechnic Institute

Research Area: Crowdsourcing Research, Participant Behavior

Discipline: Computational Social Science

Research on Prolific reveals that participant compensation significantly impacts sample selection, potentially introducing biases, and offers insights into participant motivations and behavior to improve study reliability and design.

Methods: A carefully designed experiment was performed to analyze correlations between participants' reservation wages, socioeconomic attributes, and study compensations; sensitivity analyses were conducted for further guidance.

Key Findings: Participant reservation wages, socioeconomic attributes, perceptions of general behavior and motivations, and implications of study design decisions.

Citations: 3
Credtwi: Investigating Social Media Credibility with a Browser Plugin

Authors: J Li, E Huusko, NN Ahooie, M Kuutila

Year: 2025

Published in: ... Journal of Human ..., 2025 - Taylor & Francis

Institution: University of Oulu

Research Area: Social Media Credibility, Human-Computer Interaction (HCI) in Social Media, Crowdsourcing Research

Discipline: Human-Computer Interaction (HCI)

Credtwi, a browser plugin for assessing tweet credibility, revealed that perceived Twitter credibility declines with use and author verification status heavily influences perceived credibility.

Methods: A browser plugin was used for crowdsourced credibility assessment through participant questionnaires during a week-long field study.

Key Findings: Perceptions of online tweet credibility, factors affecting tweet credibility (e.g., verification status, bio), variations in credibility assessments across genders.

DOI: https://doi.org/10.1080/10447318.2025.2480885

Citations: 2

Sample Size: 150
Data quality in crowdsourcing and spamming behavior detection

Authors: Y Ba, MV Mancenido, EK Chiou, R Pan

Year: 2025

Published in: Behavior Research Methods, 2025 - Springer

Institution: University of Delaware, National Taiwan University, University of British Columbia, Monash University

Research Area: Crowdsourcing, Data Quality, Spamming Behavior Detection, LLM Applications in Behavioral Research

Discipline: Computer Science, Artificial Intelligence, LLM

The paper introduces a systematic method to evaluate crowdsourced data quality and detect spam behaviors through variance decomposition, proposing a spammer index and credibility metrics to improve consistency and reliability in labeling tasks.

Methods: Variance decomposition, Markov chain models, and generalized random effects models were used to assess annotator consistency and credibility; metrics were applied to both simulated and real-world data from two crowdsourcing platforms.

Key Findings: Quality of crowdsourced data, spammer behaviors, annotators’ consistency, and credibility.

Citations: 2
Caution when Crowdsourcing: Prolific as a Superior Platform Compared with MTurk

Authors: D OConnell, A Bautista

Year: 2025

Published in: ... Student Journal of ..., 2025 - journals.library.columbia.edu

Institution: University of Houston, Webster University

Research Area: Crowdsourcing Research Methodology, Human-Computer Interaction (HCI)

Discipline: Computational Social Science, Behavioral Research

Prolific outperforms MTurk in participant data quality and affordability for online survey-based research.

Methods: Data from participants recruited via MTurk and Prolific were analyzed for cost, attention measures, participation duration, and internal consistency.

Key Findings: Comparison of data quality and cost-effectiveness between MTurk and Prolific for online survey recruitment.

Citations: 1

Sample Size: 699
Crowdsourcing: a modern tool for robust research sampling

Authors: JS Michel, G Sawhney, GP Watson

Year: 2025

Published in: How to Conduct and ..., 2025 - elgaronline.com

Institution: Auburn University

Research Area: Crowdsourcing, Research Methods, Social Science

Discipline: Social Science

Crowdsourcing is a versatile tool leveraging collective intelligence for efficient task completion and has applications across various fields including decentralized finance, blockchain technologies, and IO Psychology research and practice.

Methods: The paper discusses the theoretical and practical applications of crowdsourcing in various domains, referencing prior work and examples such as Wikipedia, crowdfunding platforms, and blockchain networks.

Key Findings: The applications and impact of crowdsourcing in different fields, particularly its role in Industrial-Organizational Psychology for data collection and analysis.

Citations: 1
Evaluating mobile-based data collection for crowdsourcing behavioral research

Authors: DT Esch, N Mylonopoulos, V Theoharakis

Year: 2025

Published in: Behavior Research Methods, 2025 - Springer

Institution: University of Cologne, University of Piraeus, Aristotle University of Thessaloniki

Research Area: Crowdsourcing Behavioral Research, Mobile Data Collection

Discipline: Behavioral Research

Mobile-based responses via platforms like Pollfish are comparable in quality to computer-based ones from MTurk and Prolific, though attentiveness varies significantly across platforms and is influenced by factors like incentives, distractions, and system 1 thinking.

Methods: Conducted two studies distributing the same survey across MTurk, Prolific, Pollfish, and Qualtrics panels to compare data quality and analyze attentiveness scores.

Key Findings: Attentiveness, device usage (mobile vs. computer), and factors influencing data quality such as incentives, respondent activity, distractions, and survey familiarity.

Citations: 1
Leveraging Social Media and Crowdsourcing to Recruit and Retain Military Veterans With Posttraumatic Stress Disorder or Experience of Harmful Gambling ...

Authors: C Heath, JM Williams, D Leightley

Year: 2025

Published in: JMIR mHealth and ..., 2025 - mhealth.jmir.org

Institution: Swansea University, King's College London, Reykjavík University

Research Area: mHealth Interventions, Crowdsourcing, Social Media Recruitment, Mental Health Research (PTSD, Harmful Gambling)

Discipline: Digital Health, Mental Health Research

Social media and online platforms like Facebook and Prolific were effective but faced challenges in recruiting and retaining military veterans with PTSD or harmful gambling for a digital mHealth intervention pilot study.

Methods: Multiple recruitment strategies were used, including paid and unpaid advertisements on Facebook, Prolific, direct mailing, event hosting with veterans' charities, snowball sampling, and incentives.

Key Findings: The effectiveness of different recruitment strategies for enrolling military veterans with PTSD or harmful gambling into a digital intervention study.

Sample Size: 79
Methods for a fully online automated cognitive training study on Prolific

Authors: F Joessel, S Denkinger, PE Joessel, CS Green

Year: 2025

Published in: Acta Psychologica, 2025 - Elsevier

Institution: Max Planck Institute, University of Potsdam, University of Maryland, University of Zurich, University of Arizona

Research Area: Online cognitive training, Automated psychological studies, Crowdsourcing, behavioral research

Discipline: Psychology

The study introduces a fully online method for conducting cognitive training experiments using Prolific, significantly reducing resource demands while achieving robust results and diverse participant recruitment.

Methods: Participants were recruited via Prolific, assigned to groups using a pseudo-randomized procedure, and completed a 12-hour remote cognitive training study with pre- and post-test assessments monitored via custom dashboards.

Key Findings: Impact of a 12-hour cognitive training intervention on participants' cognitive functions, conducted in a remote and automated manner.
Comparing attentional disengagement between Prolific and MTurk samples

Authors: DA Albert, D Smilek

Year: 2024

Published in: Scientific Reports, 2023 - nature.com

Institution: University of Waterloo, University of Waterloo

Research Area: Crowdsourcing Research Methods, Behavioral Science, Human-Computer Interaction (HCI)

Discipline: Psychological Science

Prolific participants exhibited lower levels of attentional disengagement compared to MTurk participants, with risk conditions and platform traits influencing task performance and disengagement.

Methods: Participants from Prolific and MTurk completed an attention task with varying error risk levels (high vs. low), and attentional disengagement was measured using task performance, self-reported mind wandering, and multitasking.

Key Findings: Attentional disengagement through task performance, mind wandering, and multitasking under different risk conditions across two recruitment platforms (Prolific and MTurk).

Citations: 150

Sample Size: 80
Crowdsourcing samples for research on violent extremism: A research note

Authors: C Clemmow, I van der Vegt, B Rottweiler

Year: 2024

Published in: ... and political violence, 2024 - Taylor & Francis

Institution: University College London

Research Area: Crowdsourcing for Violent Extremism Research

Discipline: Computational Social Science

Citations: 12
Generative AI in crowdwork for web and social media research: A survey of workers at three platforms

Authors: E Christoforou, G Demartini

Year: 2024

Published in: Proceedings of the ..., 2024 - ojs.aaai.org

Institution: University of Sheffield, University of Southampton

Research Area: Crowdsourcing, Generative AI, Web and Social Media Research, LLM

Discipline: Artificial Intelligence

DOI: https://doi.org/10.1609/icwsm.v18i1.31452

Citations: 10
Insights from an experiment crowdsourcing data from thousands of US Amazon users: The importance of transparency, money, and data use

Authors: A Berke, R Mahari, A Pentland, K Larson

Year: 2024

Published in: Proceedings of the ACM ..., 2024 - dl.acm.org

Institution: Stanford's CodeX Center, Harvard Law School, MIT Media Lab, Stanford Institute for Human-Centered AI, The Larson Institute, Massachusetts Institute of Technology, Stanford University

Research Area: Crowdsourcing, Transparency, Human-Computer Interaction (HCI) in Social Science Research

Discipline: Computational Social Science, Human-Computer Interaction (HCI)

DOI: https://dl.acm.org/doi/abs/10.1145/3687005

Citations: 9
Can crowdsourcing platforms be useful for educational research?

Authors: KD Wang, Z Chen, C Wieman

Year: 2024

Published in: ... of the 14th Learning Analytics and ..., 2024 - dl.acm.org

Institution: Delft University of Technology, University of Queensland

Research Area: Crowdsourcing for Educational Research

Discipline: Educational Research, Computer Science

Citations: 8
Crowdsourcing Academic Online Research on Prolific

Authors: Eyal Peer

Year: 2024

Published in: CAMBRIDGE

Institution: Hebrew University, University of Cambridge

Research Area: Crowdsourcing, Research Methodology in Behavioral and Social Sciences

Discipline: Social, Behavioral Sciences

Citations: 7
Crowdsourcing technology to support academic research

Authors: M Hirth, J Jacques, P Rodgers, O Scekic

Year: 2023

Published in: Evaluation in the Crowd ..., 2017 - Springer

Research Area: Crowdsourcing, Research Methodology, Human-Computer Interaction (HCI) in Academic Research

Discipline: Social Science (specifically Crowdsourcing, Human-Computer Interaction (HCI), Research Methodology)

Citations: 15
Best practices and ethical considerations for crowd-sourced data in the behavioral sciences

Authors: R Kapitany, C Kavanagh

Year: 2023

Published in: 2023 - OSF

Research Area: Crowdsourcing ethics, best practices in behavioral science research

Discipline: Behavioral Science

Citations: 4
The Use of Mechanical Turk Data in Psychological Research.

Authors: SG Lopez, SV Rouse

Year: 2023

Published in: Psi Chi Journal of ..., 2023 - search.ebscohost.com

Research Area: Crowdsourcing, Mechanical Turk in Psychological Research

Discipline: Psychological Research

Citations: 2
Recruiting software engineers on prolific

Authors: D Russo

Year: 2022

Published in: arXiv preprint arXiv:2203.14695, 2022 - arxiv.org

Institution: Università della Svizzera italiana

Research Area: Crowdsourcing, Software Engineering Research, Participant Recruitment

Discipline: Software Engineering

Citations: 30
The state of pilot study reporting in crowdsourcing: A reflection on best practices and guidelines

Authors: J Oppenlaender, T Abbas, U Gadiraju

Year: 2022

Published in: ... of the ACM on Human-Computer ..., 2024 - dl.acm.org

Institution: University of Oulu, University of Tübingen, Aston University, Delft University of Technology

Research Area: Crowdsourcing, Research Best Practices, Human-Computer Interaction (HCI)

Discipline: Human-Computer Interaction (HCI)

The paper shows that pilot studies are widely used but poorly reported in crowdsourcing and HCI research, making studies harder to interpret, replicate, and trust. It proposes clear reporting guidelines to improve transparency, rigor, and data quality in human-subject experiments.

Citations: 6