Explore 5 peer-reviewed studies by M Ku in Social Media Credibility and Human-Computer Interaction (HCI) (2020–2025). Discover research powered by Prolific's participant panel.
This page lists 5 peer-reviewed papers authored or co-authored by M Ku in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: J Li, M Kuutila, E Huusko, N Kariyakarawana
Year: 2025
Published in: Proceedings of the 15th ..., 2023 - dl.acm.org
Institution: University of Oulu
Research Area: Social Media Credibility, Crowdsourcing, Human-Computer Interaction (HCI)
Discipline: Human-Computer Interaction (HCI)
Credibility of short-form health-related social media posts is influenced by factors such as author profession and post engagement metrics, with experts being encouraged to actively participate in information correction online.
Methods: Crowdsourced online credibility assessment using health-themed social media posts with varied content features deployed across three platforms; quantitative and qualitative data collection.
Key Findings: Credibility factors like author profession, engagement metrics (likes/shares), and personal strategies influencing perceived trustworthiness of social media posts.
DOI: 10.1145/3605390.3605406
Citations: 11
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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
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Authors: M Ku, T Li, K Zhang, Y Lu, X Fu, W Zhuang
Year: 2024
Published in: - arXiv preprint arXiv …, 2023 - arxiv.org
Institution: University of Waterloo, Ohio State University, University of California Santa Barbara, University of Pensylvania
Research Area: AI alignment, Representation learning, Cognitive computational modeling, Vision foundation models evaluation, Multimodal, Vision models
Discipline: Computer Science, Artificial Intelligence, Machine Learning
This paper presents a method for **aligning machine vision model representations with human visual similarity judgments across different abstraction levels, improving how well models reflect human perceptual and conceptual organization and enhancing generalization and uncertainty prediction.
DOI: https://doi.org/10.48550/arXiv.2310.01596
Citations: 59
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Authors: M Kuutila, C Kiili, R Kupiainen, E Huusko, J Li
Year: 2024
Published in: Computers in Human ..., 2024 - Elsevier
Research Area: Social Media Credibility Evaluation, Human-Computer Interaction (HCI), Cyberpsychology, AI Evaluation
Discipline: Computer science, human–computer interaction, cyberpsychology
The study found that prior belief consistency and source expertise significantly influenced perceived credibility of health-related social media posts, while evidence quality had minimal impact. Crowdsourcing platform choice also affected credibility evaluations of inaccurate posts.
Methods: Researchers created social media posts with manipulated source characteristics, claim accuracy, and evidence quality. Participants evaluated the credibility of these posts via crowdsourcing platforms after having their prior topic beliefs assessed.
Key Findings: The perceived credibility of health-related social media posts based on source characteristics, evidence quality, prior beliefs, and the platform used for data collection.
DOI: https://doi.org/10.1016/j.chb.2023.108017
Citations: 19
Sample Size: 844
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Authors: J Hanson, M Wei, S Veys, M Kugler
Year: 2020
Published in: Proceedings of the ..., 2020 - dl.acm.org
Institution: University of Chicago, University of Washington
Research Area: Privacy, Crowdwork, Hyper-Personalization in Advertising
Discipline: Human-Computer Interaction (HCI)
DOI: https://doi.org/10.1145/3313831.3376415
Citations: 40