2023/03/27 by Gabriel Asher, Asher, Gabriel, Phil Bohlman +3
Computer Science · Social Sciences · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Machine Learning (cs.LG) #Misinformation and Its Impacts #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2303.16777
openalex publication_date 2023/03/27 · openalex created_date 2023/04/05 · openalex updated_date 2026/07/28
COVID-19 misinformation on social media platforms such as twitter is a threat to effective pandemic management. Prior works on tweet COVID-19 misinformation negates the role of semantic features common to twitter such as charged emotions. Thus, we present a novel COVID-19 misinformation model, which uses both a tweet emotion encoder and COVID-19 misinformation encoder to predict whether a tweet contains COVID-19 misinformation. Our emotion encoder was fine-tuned on a novel annotated dataset and our COVID-19 misinformation encoder was fine-tuned on a subset of the COVID-HeRA dataset. Experimental results show superior results using the combination of emotion and misinformation encoders as opposed to a misinformation classifier alone. Furthermore, extensive result analysis was conducted, highlighting low quality labels and mismatched label distributions as key limitations to our study.