Identifying and classifying COVID-19 stigma on social media

Abstract

Since the introduction of COVID-19 in early 2020, COVID-19 stigma has persisted on social media. Stigma, a social process where individuals or groups are labeled, stereotyped, and separated, can result in misinformation, discrimination, and violence. The body of research on COVID-19 stigma is growing, but addressing stigma on social media remains challenging because of the enormous volume and diversity of rapidly changing content. This three-part methodology offers a standardized approach for generating (1) a relevant and manageable social media sample for stigma identification and research, (2) a categorization process to organize the sample, and (3) a systematic coding method for classifying stigma within the sample. An application of the methodology generated a curated sample of 138,998 posts from Twitter and Reddit, organized according to key stigma domain, key terms, frequency of terms, and hashtag occurrence. A subset of 711 posts were selected for the content analysis and analyzed based on the key stigma domains, distinguishing between intentional and unintentional stigma. This methodology has the potential to facilitate comprehensive social media stigma research through simplified sample generation and stigma identification processes and offers the possibility of adaptation to address other types of social media stigma, beyond COVID-19.