Discover 13 peer-reviewed studies in Natural Language Processing (2022–2025). Explore research findings powered by Prolific's diverse participant panel.
This page lists 13 peer-reviewed papers in the research area of Natural Language Processing in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
-
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: P Schmidtová, O Dušek, S Mahamood
Year: 2025
Published in: ArXiv
Institution: Charles University, Trivago
Research Area: Summarization evaluation, Natural Language Processing, LLM-as-a-Judge, AI Evaluation
Discipline: Natural Language Processing
Simpler metrics like word overlap surprisingly correlate well with human judgments in summarization evaluation, outperforming complex methods in out-of-domain applications, though LLMs remain unreliable for assessment due to annotation biases.
Methods: Human evaluation campaigns with categorical error assessment, span-level annotations, and comparison of traditional metrics, trainable models, and LLM-as-a-judge approaches.
Key Findings: Effectiveness of summarization evaluation methods and their correlation with human judgment, along with business impacts of incorrect information in generated summaries.
Citations: 1
-
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.
-
Authors: Daria Kryvosheieva
Year: 2024
Published in: ArXiv
Institution: Massachusetts Institute of Technology
Research Area: Natural Language Processing, AI Evaluation
Discipline: Natural Language Processing
-
Authors: Md. Khairul Islam1, Andrew Wang1, Tianhao Wang1, Yangfeng Ji1, Judy Fox 1, Jieyu Zhao2
Year: 2024
Published in: ArXiv
Institution: University of Virginia
Research Area: Differential Privacy, Bias Mitigation, LLM, Natural Language Processing (NLP), AI Bias
Discipline: Artificial Intelligence, Natural Language Processing
-
Authors: Yuxin Wang♣ Xiaomeng Zhu◆ Weimin Lyu♠∗ Saeed Hassanpour♣ Soroush Vosoughi♣
Year: 2024
Published in: ArXiv
Institution: Department of Computer Science Dartmouth College, Stony Brook University, Yale University
Research Area: Natural Language Processing, Computational Linguistics
Discipline: Natural Language Processing
-
Authors: Jacob Beck, Stephanie Eckman, Bolei Ma, Rob Chew, Frauke Kreuter
Year: 2024
Published in: ACL Anthology
Institution: University of Maryland
Research Area: Annotation Sensitivity, Order Effects, Natural Language Processing, Social Science in AI
Discipline: Natural Language Processing (NLP), Computational Social Science
-
Authors: Quan Ze Chen K.J. Kevin Feng Chan Young Park Amy X. Zhang
Year: 2024
Published in: ArXiv
Institution: University of Washington
Research Area: In-Context Learning, Computational Linguistics, Natural Language Processing
Discipline: Computer Science, Computational Linguistics, Natural Language Processing
-
Authors: O Raccah, P Chen, TM Gureckis, D Poeppe, VA Vo
Year: 2024
Published in: Nature
Institution: Intel Labs, New York University, Yale University
Research Area: Cognitive Psychology, Memory Research, Natural Language Processing (NLP)
Discipline: Psychology, Artificial Intelligence
-
Authors: J Barua, S Subramanian, K Yin, A Suhr
Year: 2024
Published in: ArXiv
Institution: University of California Berkeley
Research Area: Natural Language Processing (NLP), Machine Translation, Lexical Semantics
Discipline: Natural Language Processing
-
Authors: J Pei, D Jurgens
Year: 2023
Published in: arXiv preprint arXiv:2306.06826, 2023 - arxiv.org
Institution: University of Michigan, University of Toronto
Research Area: Natural Language Processing (NLP)
Discipline: Natural Language Processing, Human-Computer Interaction (HCI)
DOI: https://doi.org/10.48550/arXiv.2306.06826
Citations: 55
-
Authors: G Abercrombie, D Hovy
Year: 2023
Published in: 17th Linguistic ..., 2023 - researchportal.hw.ac.uk
Institution: Heriot Watt University
Research Area: Hate Speech Annotation, Computational Linguistics, Natural language processing (NLP), Annotation
Discipline: Computational Linguistics
DOI: https://doi.org/10.18653/v1/2023.law-1.10
Citations: 23
-
Authors: LE Ruis, A Khan, S Biderman, S Hooker, T Rocktäschel
Year: 2022
Published in: 2022 - openreview.net
Institution: MILA, University of Toronto, Stanford University, Hugging Face, Imperial College London
Research Area: Natural Language Processing, LLM, Communication
Discipline: Natural Language Processing
Citations: 52