2024/07/30 by Pujan Paudel, Ling Chen, Paudel, Pujan +5 · 1 citation
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection
paper · pdf · doi:10.48550/arxiv.2407.20987
openalex publication_date 2024/07/30 · openalex created_date 2024/08/01 · openalex updated_date 2026/07/28
Images are a powerful and immediate vehicle to carry misleading or outright false messages, yet identifying image-based misinformation at scale poses unique challenges. In this paper, we present PIXELMOD, a system that leverages perceptual hashes, vector databases, and optical character recognition (OCR) to efficiently identify images that are candidates to receive soft moderation labels on Twitter. We show that PIXELMOD outperforms existing image similarity approaches when applied to soft moderation, with negligible performance overhead. We then test PIXELMOD on a dataset of tweets surrounding the 2020 US Presidential Election, and find that it is able to identify visually misleading images that are candidates for soft moderation with 0.99% false detection and 2.06% false negatives.