Topic Modeling of Mpox-related Instagram Posts: Understanding Public Perception over Time

Mpox is now a pandemic disease, and all its public health concerns have inspired very heated debates on Instagram. This paper is trying to capture possible public perceptions, concerns, and narratives through their Instagram posts on Mpox by applying two cutting-edge unsupervised topic models called BERTopic and Latent Dirichlet Allocation (LDA). A dataset of 60127 multilingual posts from July 2022 to October 2024 was processed and raised some clear thematic issues and vaccination hesitance, including disease awareness, awareness of misinformation, and geographic differences. BERTopic investigated nuanced location-specific issues such as humor on social media and technical risks; LDA gave a more extensive framework centered on broader topics like health emergencies and worldwide impact. The base of the analysis is a dataset of Mpox-related Instagram posts collected over a specified time. Pre-processing consists of text extraction and removal of URLs, tokenization, and removal of stop words, and also uses countVectorization. These findings serve to shed light on the interaction between public sentiment and health communication and the critical importance of customized outreach programs to counter misinformation and enhance awareness and public health response in outbreaks of infectious diseases. Findings yield insights into public health communication in setting proactive plans to curb misinformation and boost awareness, with future studies along the lines of cross-platform analysis as well as with multi-media data touching on the greater public perception.