In Public health
OBJECTIVES : This study aimed to study the public's sentiments on the current monkeypox outbreaks via an unsupervised machine learning analysis of social media posts.
STUDY DESIGN : This was an exploratory analysis of tweets sentiments.
METHODS : We extracted original tweets containing the terms 'monkeypox', 'monkey pox' or 'monkey_pox' and posted them in the English language from 6 May 2022 (first case detected in the United Kingdom) to 23 July 2022 (when World Health Organization declared Monkeypox to be a global health emergency). Retweets and duplicate tweets were excluded from study. Bidirectional Encoder Representations from Transformers (BERT) Named Entity Recognition. This was followed by topic modelling (specifically BERTopic) and manual thematic analysis by the study team, with independent reviews of the topic labels and themes.
RESULTS : Based on topic modelling and thematic analysis of a total of 352,182 Twitter posts, we derived five topics clustered into three major themes related to the public discourse on the ongoing outbreaks. These include concerns of safety, stigmatisation of minority communities, and a general lack of faith in public institutions. The public sentiments underscore growing (and existing) partisanship, personal health worries in relation to the evolving situation, as well as concerns of the media's portrayal of lesbian, gay, bisexual, transgender and queer and minority communities, which might further stigmatise these groups.
CONCLUSIONS : Monkeypox is an emerging infectious disease of public concern. Our study has highlighted important societal issues, including misinformation, political mistrust and anti-gay stigma that should be sensitively considered when designing public health policies to contain the ongoing outbreaks.
Ng Q X, Yau C E, Lim Y L, Wong L K T, Liew T M
2022-Oct-25
BERT, Monkeypox, Social media, Stigma, Topic modelling
