Stigmatization of mpox and the MSM community on Twitter: A computational approach to exploring the negative emotions expressed through stigmatizing language

Abstract

Men who have sex with men (MSM) are disproportionately affected by mpox, which may impact experiences of stigma and reinforce harmful stereotypes. Based on the theoretical propositions of the model of stigma communication (MSC), we investigate the stigmatizing language and emotions expressed on Twitter (N = 1,285,691) through emotion classification and semantic network analyses. Results indicate that fear was the most common stigma-related emotion present (39%), followed by anger (8%) and disgust (3%). While posts expressing fear focused on the disease itself, posts expressing anger and disgust primarily targeted the gay community. Findings reveal the significance of understanding how negative emotions expressed online can amplify stigma against minoritized communities.