Discover 6 peer-reviewed studies in Affective Computing (2022–2025). Explore research findings powered by Prolific's diverse participant panel.
This page lists 6 peer-reviewed papers in the research area of Affective Computing in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
-
Authors: U Messer
Year: 2025
Published in: Computers in Human Behavior: Artificial Humans, 2025 - Elsevier
Institution: Universität der Bundeswehr München
Research Area: Political Bias in Generative AI, Human-AI Interaction, Affective Computing, AI Bias
Discipline: Computer Science, Human-AI Interaction
People's acceptance and reliance on Generative AI (GAI) increase when they perceive alignment between their political orientation and the bias of GAI-generated content, leading to expanded trust in sensitive applications.
Methods: Three experiments analyzing behavioral reactions to politically biased content generated by GAI, including the impact of perceived alignment on acceptance and trust.
Key Findings: Participants' acceptance, reliance, and trust in GAI based on perceived alignment between political bias of GAI-generated content and their own political beliefs.
DOI: https://doi.org/10.1016/j.chbah.2024.100108
Citations: 24
Sample Size: 513
-
Authors: T Hu, N Collier
Year: 2025
Published in: arXiv preprint arXiv:2503.03335, 2025 - arxiv.org
Institution: University of Cambridge
Research Area: Affective Computing, Natural Language Processing, Computational Social Science
Discipline: Computational Social Science
The iNews dataset is a multimodal resource for studying personalized affective responses to news, improving modeling accuracy by incorporating annotator persona metadata.
Methods: 292 demographically diverse UK participants annotated 2,899 Facebook news posts with multidimensional labels (e.g., emotions, valence, arousal), combined with comprehensive participant persona data.
Key Findings: Modeled personalized affective responses to news through annotations capturing valence, arousal, emotions, and persona metadata.
Citations: 2
Sample Size: 2899
-
Authors: JD Lomas, W van der Maden, S Bandyopadhyay
Year: 2024
Published in: Advanced Design ..., 2024 - Elsevier
Institution: Delft University of Technolog, Playpower Labs, Hong Kong Polytechnic University, Utrecht University
Research Area: AI Alignment, Affective Computing, Emotional Expression in Generative AI, Human Perception of AI Emotions
Discipline: Affective Computing, Artificial Intelligence, Human-Computer Interaction (HCI)
This study evaluates how well generative AI systems (like DALL·E 2/3 and Stable Diffusion) can generate emotionally expressive content that aligns with how humans perceive those emotions, finding that model performance varies by emotion type and model, with implications for designing more emotionally aligned AI.
DOI: https://doi.org/10.1016/j.ijadr.2024.10.002
Citations: 5
-
Authors: JD Lomas, W van der Maden
Year: 2024
Published in: arXiv preprint arXiv ..., 2024 - arxiv.org
Institution: Delft University of Technology, Microsoft Research
Research Area: Affective Computing, Human-AI Interaction, Image Generation
Discipline: Artificial Intelligence
DOI: https://doi.org/10.48550/arXiv.2405.18510
Citations: 5
-
Authors: Jen-tse Huang, Man Ho Lam, Eric John Li, Shujie Ren, Wenxuan Wang, Wenxiang Jiao, Zhaopeng Tu, Michael R. Lyu
Year: 2024
Published in: Preprint
Institution: Chinese University of Hong Kong, Tianjin Medical University
Research Area: LLM Emotional Evaluation, Affective Computing, Artificial Intelligence in Psychology
Discipline: Artificial Intelligence
-
Authors: TXF Seow, TU Hauser
Year: 2022
Published in: Behavior research methods, 2022 - Springer
Institution: University College London, Max Planck University College London
Research Area: Behavioral Research Methods, Affective Computing, Web-based Experimentation
Discipline: Human-Computer Interaction (HCI)
Citations: 23