Authors: Y Ding, J You, TK Machulla, J Jacobs, P Sen
Year: 2025
Published in: Proceedings of the ..., 2022 - dl.acm.org
Institution: University of California Irvine, University of Florida, State University of New York at Buffalo, University of Waterloo, Virginia Tech
Research Area: Computational Social Science, Human-Computer Interaction (HCI), Sentiment Analysis
Discipline: Computational Social Science
Demographic differences among annotators significantly affect sentiment dataset labels, causing up to a 4.5% accuracy difference in sentiment prediction models.
Methods: Crowdsourced annotations from >1000 workers combined with demographic data; analysis of multimodal sentiment datasets and evaluation using machine learning models.
Key Findings: Impact of annotator demographics on sentiment labeling and its effect on model predictions.
DOI: https://doi.org/10.1145/3555632
Citations: 28
Sample Size: 1000