Explore 5 peer-reviewed studies by J Li in Human-Computer Interaction and Social Media Credibility (2023–2025). Discover research powered by Prolific's participant panel.
This page lists 5 peer-reviewed papers authored or co-authored by J Li in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
-
Authors: P. Schoenegger, F. Salvi, J. Liu, X. Nan, R. Debnath, B. Fasolo, E. Leivada, G. Recchia, F. Günther, A. Zarifhonarvar, J. Kwon, Z. Ul Islam, M. Dehnert, D. Y. H. Lee, M. G. Reinecke, D. G. Kamper, M. Kobaş, A. Sandford, J. Kgomo, L. Hewitt, S. Kapoor, K. Oktar, E. E. Kucuk, B. Feng, C. R. Jones, I. Gainsburg, S. Olschewski, N. Heinzelmann, F. Cruz, B. M. Tappin, T. Ma, P. S. Park, R. Onyonka, A. Hjorth, P. Slattery, Q. Zeng, L. Finke, I. Grossmann, A. Salatiello, E. Karger
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: London School of Economics and Political Science, University of Cambridge, University College London, Massachusetts Institute of Technology, University of Oxford, Modulo Research, Stanford University, Federal Reserve Bank of Chicago, ETH Zürich, University of Johannesburg
Research Area: Natural Language Processing
Discipline: Social Science, Artificial Intelligence
This paper compares a frontier LLM (Claude Sonnet 3.5) against incentivized human persuaders in a conversational quiz setting, finding that the AI's persuasion capabilities surpass those of humans with real-money bonuses tied to performance.
Citations: 16
-
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
Discipline: Human-Computer Interaction
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
-
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
Discipline: Human-Computer Interaction
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
-
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, 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
-
Authors: J Li, V Paananen, SA Suryanarayana
Year: 2023
Published in: Proceedings of the ..., 2023 - dl.acm.org
Institution: University of Oulu, University of Helsinki
Research Area: Twitter Credibility, Machine Learning Tools, Online Behavior, Human-Computer Interaction
Discipline: Computational Social Science
DOI: https://doi.org/10.1145/3576840.3578308
Citations: 8
