King S College London: Research Institution — Prolific Citations Library

Browse 4 peer-reviewed papers from King S College London spanning mHealth Interventions, Crowdsourcing (2022–2025). Research powered by Prolific's high-quality participant data.

This page lists 4 peer-reviewed papers from researchers at King S College London in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (4 of 4)

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
As Good As A Coin Toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli

Authors: D Cooke, A Edwards, S Barkoff, K Kelly

Year: 2024

Published in: arXiv preprint arXiv:2403.16760, 2024 - arxiv.org

Institution: King’s College London, Center for Strategic and International Studies

Research Area: Detection of AI-Generated Multimedia, Misinformation, Human-Computer Interaction (HCI)

Discipline: Human-Computer Interaction (HCI), Artificial Intelligence, Media Studies

People struggle to distinguish AI-generated media from authentic content, with detection accuracy averaging close to chance (50%), and performance declines in certain conditions like synthetic human faces, foreign languages, or heterogeneous media modalities.

Methods: Perceptual study evaluating participants' ability to identify authentic and synthetic images, audio, video, and audiovisual stimuli under various conditions.

Key Findings: Human ability to detect AI-generated media compared to authentic media, including factors like modality, content type, foreign languages, and participant demographics.

DOI: https://doi.org/10.48550/arXiv.2403.16760

Citations: 44

Sample Size: 1276
Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback

Authors: HR Kirk, B Vidgen, P Röttger, SA Hale

Year: 2023

Published in: arXiv preprint arXiv:2303.05453, 2023 - arxiv.org

Institution: The Alan Turing Institute, University of Oxford, Imperial College London, King's College London, Google DeepMind

Research Area: Large Language Model Alignment, Safety, Personalization Risks

Discipline: Artificial Intelligence

DOI: https://doi.org/10.48550/arXiv.2303.05453

Citations: 146
Intersectional experiences of unfair treatment caused by automated computational systems

Authors: T Van Nuenen, J Such, M Cote

Year: 2022

Published in: Proceedings of the ACM on human ..., 2022 - dl.acm.org

Institution: University of Surrey, King’s College London, Tilburg University, University of Amsterdam

Research Area: Intersectional Fairness, Automated Systems, Social Computing

Discipline: Computational Social Science

Citations: 23