Queen S University: Research Institution — Prolific Citations Library

Browse 7 peer-reviewed papers from Queen S University spanning Natural Language Processing, Harmful Content Detection (2021–2025). Research powered by Prolific's high-quality participant data.

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

Papers (7 of 7)

LLM-based Semantic Augmentation for Harmful Content Detection

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.
Human participants in AI research: Ethics and transparency in practice

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 (HCI), Research Practice Transparency

Discipline: AI Ethics, Human-Computer Interaction (HCI)

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
Can crowdsourcing platforms be useful for educational research?

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
On the role of large language models in crowdsourcing misinformation assessment

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, LLM

Discipline: Artificial Intelligence

DOI: https://doi.org/10.1609/icwsm.v18i1.31417

Citations: 6
Other Worlds: Using AI to Revisit Cybersyn and Rethink Economic Futures

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
Software engineering user study recruitment on Prolific: An experience report

Authors: B Reid, M Wagner, M d'Amorim, C Treude

Year: 2022

Published in: arXiv preprint arXiv ..., 2022 - arxiv.org

Institution: University of Sydney, Queen's University, University of Victoria, University of Calgary

Research Area: Software Engineering User Studies, Crowdsourcing Platforms (Prolific)

Discipline: Software Engineering, Human-Computer Interaction (HCI)

Citations: 35
How Does Citizen Science Compare to Online Survey Panels? A Comparison of Food Knowledge and Perceptions Between the Zooniverse, Prolific and Qualtrics UK Panels

Authors: Beth Armstrong, Christian Reynolds, Gemma Bridge, Libby Oakden Changqiong Wang, Luca Panzone, Ximena Schmidt Rivera, Astrid Kause, Charles Ffoulkes, Coleman Krawczyk, Grant Miller, Stephen Serjeant

Year: 2021

Published in: Frontiers

Institution: Agricultural Development Advisory Service, Brunel University London, Leeds Beckett University, Newcastle University, Open University, Queen Mary University of London, The University of Sheffield, University of Leeds, University of London, University of Portsmouth, Zooniverse

Research Area: Food Knowledge, Citizen Science, Survey Methodology

Discipline: Food Science, Survey Methodology

Citations: 26