Browse 5 peer-reviewed papers from Queens University spanning Natural Language Processing, Harmful Content Detection (2024–2025). Research powered by Prolific's high-quality participant data.
This page lists 5 peer-reviewed papers from researchers at Queens University in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: Elyas Meguellati1, Assad Zeghina2, Shazia Sadiq1, Gianluca Demartini1
Year: 2025
Published in: ArXiv
Institution: University of Queensland, University of Strasbourg
Research Area: Natural Language Processing, Harmful Content Detection
Discipline: Natural Language Processing
The paper introduces an approach using LLM-based semantic augmentation for harmful content detection on social media, achieving performance comparable to human-annotated models but at reduced cost.
Methods: The researchers utilize LLMs to clean noisy text and generate explanations for context-rich preprocessing, then evaluate the augmented training sets on multiple high-context datasets such as SemEval 2024 Persuasive Meme, Google Jigsaw toxic comments, and Facebook hateful memes datasets.
Key Findings: The efficacy of LLM-based semantic augmentation in enhancing training sets for social media tasks such as propaganda detection, hateful meme classification, and toxicity identification.
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Authors: KR McKee
Year: 2024
Published in: IEEE Transactions on Technology and Society, 2024 - ieeexplore.ieee.org
Institution: University of Queensland
Research Area: AI Ethics, Human-Computer Interaction, Research Practice Transparency
Discipline: AI Ethics, Human-Computer Interaction
The paper identifies ethical and transparency gaps in AI research involving human participants and proposes guidelines to address these issues, drawing from adjacent fields like psychology and human-computer interaction while recognizing unique challenges in AI contexts.
Methods: Analyzed normative practices by reviewing AI research publications and compared them with ethical standards in adjacent fields such as psychology and HCI.
Key Findings: Ethical practices including ethical reviews, informed consent, participant compensation, and contextual considerations specific to AI research.
DOI: https://ieeexplore.ieee.org/abstract/document/10664609/
Citations: 17
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Authors: KD Wang, Z Chen, C Wieman
Year: 2024
Published in: ... of the 14th Learning Analytics and ..., 2024 - dl.acm.org
Institution: Delft University of Technology, University of Queensland
Research Area: Crowdsourcing for Educational Research
Discipline: Educational Research, Computer Science
Citations: 8
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Authors: J Xu, L Han, S Sadiq, G Demartini
Year: 2024
Published in: Proceedings of the International ..., 2024 - ojs.aaai.org
Institution: University of Lausanne, EPFL, University of Southampton, University of Queensland
Research Area: Crowdsourcing, Misinformation Assessment, Large Language Models
Discipline: Artificial Intelligence
DOI: https://doi.org/10.1609/icwsm.v18i1.31417
Citations: 6
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Authors: L Munn, L Magee
Year: 2024
Published in: ArXiv
Institution: University of Illinois Urbana Champaign, University of Queensland
Research Area: AI and Economic Futures, Cybernetics, Social Science
Discipline: Artificial Intelligence, Economics
