University Of Amsterdam: Research Institution — Prolific Citations Library

Browse 7 peer-reviewed papers from University Of Amsterdam spanning Social media, Misinformation (2022–2025). Research powered by Prolific's high-quality participant data.

This page lists 7 peer-reviewed papers from researchers at University Of Amsterdam in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (7 of 7)

Tokenization of social media engagements increases the sharing of false (and other) news but penalization moderates it

Authors: M Alizadeh, E Hoes, F Gilardi

Year: 2025

Published in: Scientific Reports, 2023 - nature.com

Institution: Department of Marketing, University of Amsterdam, Department of Social Sciences, Università Degli Studi di Milano, Department of Political Science and International Relations, Università Degli Studi di Milano

Research Area: Social media, Misinformation, Computational Social Science

Discipline: Computational Social Science

Token-based incentives for social media engagement increase the sharing of misinformation, but implementing penalties for objectionable content can reduce this trend without fully eliminating it.

Methods: Survey experiment analyzing the impact of hypothetical token rewards and penalties on user willingness to share different types of news content.

Key Findings: Effect of token-based incentives and penalties on user engagement and the willingness to share misinformation.

DOI: https://doi.org/10.1038/s41598-023-40716-2

Citations: 20
Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback: AD Lindström et al.

Authors: A Dahlgren Lindström, L Methnani, L Krause

Year: 2025

Published in: Ethics and Information ..., 2025 - Springer

Institution: Umeå University, Vrije Universiteit Amsterdam

Research Area: AI Alignment, AI Safety, Reinforcement Learning from Human Feedback (RLHF), Sociotechnical Systems

Discipline: Artificial Intelligence, Ethics

The paper critiques AI alignment efforts using RLHF and RLAIF, highlighting theoretical and practical limitations in meeting the goals of helpfulness, harmlessness, and honesty, and advocates for a broader sociotechnical approach to AI safety and ethics.

Methods: Sociotechnical critique of RLHF techniques with an analysis of theoretical frameworks and practical implementations.

Key Findings: The alignment of AI systems with human values and the efficacy of RLHF techniques in achieving the HHH principle (helpfulness, harmlessness, honesty).

DOI: https://doi.org/10.1007/s10676-025-09837-2

Citations: 14
Surveys considered harmful? Reflecting on the use of surveys in AI research, development, and governance

Authors: M Tahaei, D Wilkinson, A Frik, M Muller

Year: 2024

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

Institution: University of Cambridge, University of Bath, University of Amsterdam, Amazon

Research Area: AI Ethics, Survey Methods, AI Governance

Discipline: AI Ethics, Governance

DOI: https://doi.org/10.1609/aies.v7i1.31734

Citations: 11
Can Large Language Models Generalize Analogy Solving Like Children Can?

Authors: Claire E. Stevenson⋄

Year: 2024

Published in: ArXiv

Institution: Santa Fe Institute, University of Amsterdam

Research Area: LLM Generalization, Analogy Solving, Cognitive Development, Artificial Intelligence in Psychology

Discipline: Cognitive Science, Artificial Intelligence
Evaluating Creative Short Story Generation in Humans and Large Language Models

Authors: Mete Ismayilzada1,2, Claire Stevenson3, Lonneke van der Plas

Year: 2024

Published in: ArXiv

Institution: Idiap Research Institute, University of Amsterdam, Università della Svizzera Italiana, École Polytechnique Fédérale de Lausanne

Research Area: Creative Story Generation, LLM Evaluation, Computational Creativity

Discipline: Artificial Intelligence, Natural Language Processing, Computational Creativity
The role of response conflict in concealed information detection with reaction times

Authors: Nathalie klein Selle, Barak Or, Ine Van der Cruyssen, Bruno Verschuere & Gershon Ben-Shakhar

Year: 2023

Published in: Nature

Institution: Bar-Ilan University, Hebrew University of Jerusalem, University of Amsterdam

Research Area: Concealed Information Test (CIT), Response Conflict, Reaction Time Analysis

Discipline: Psychology
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