Mpox Panic, Infodemic, and Stigmatization of the Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual Community: Geospatial Analysis, Topic Modeling, and Sentiment Analysis of a Large, Multilingual Social Media Database

Abstract

Background The global Mpox (formerly, Monkeypox) outbreak is disproportionately affecting the gay and bisexual men having sex with men community. Objective The aim of this study is to use social media to study country-level variations in topics and sentiments toward Mpox and Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual (2SLGBTQIAP+)–related topics. Previous infectious outbreaks have shown that stigma intensifies an outbreak. This work helps health officials control fear and stop discrimination. Methods In total, 125,424 Twitter and Facebook posts related to Mpox and the 2SLGBTQIAP+ community were extracted from May 1 to December 25, 2022, using Twitter application programming interface academic accounts and Facebook-scraper tools. The tweets’ main topics were discovered using Latent Dirichlet Allocation in the sklearn library. The pysentimiento package was used to find the sentiments of English and Spanish posts, and the CamemBERT package was used to recognize the sentiments of French posts. The tweets’ and Facebook posts’ languages were understood using the Twitter application programming interface platform and pycld3 library, respectively. Using ArcGis Online, the hot spots of the geotagged tweets were identified. Mann-Whitney U, ANOVA, and Dunn tests were used to compare the sentiment polarity of different topics and countries. Results The number of Mpox posts and the number of posts with Mpox and 2SLGBTQIAP+ keywords were 85% correlated (P<.001). Interestingly, the number of posts with Mpox and 2SLGBTQIAP+ keywords had a higher correlation with the number of Mpox cases (correlation=0.36, P<.001) than the number of posts on Mpox (correlation=0.24, P<.001). Of the 10 topics, 8 were aimed at stigmatizing the 2SLGBTQIAP+ community, 3 of which had a significantly lower sentiment score than other topics (ANOVA P<.001). The Mann-Whitney U test shows that negative sentiments have a lower intensity than neutral and positive sentiments (P<.001) and neutral sentiments have a lower intensity than positive sentiments (P<.001). In addition, English sentiments have a higher negative and lower neutral and positive intensities than Spanish and French sentiments (P<.001), and Spanish sentiments have a higher negative and lower positive intensities than French sentiments (P<.001). The hot spots of the tweets with Mpox and 2SLGBTQIAP+ keywords were recognized as the United States, the United Kingdom, Canada, Spain, Portugal, India, Ireland, and Italy. Canada was identified as having more tweets with negative polarity and a lower sentiment score (P<.04). Conclusions The 2SLGBTQIAP+ community is being widely stigmatized for spreading the Mpox virus on social media. This turns the community into a highly vulnerable population, widens the disparities, increases discrimination, and accelerates the spread of the virus. By identifying the hot spots and key topics of the related tweets, this work helps decision makers and health officials inform more targeted policies.

Investigation of the Misinformation about COVID-19 on YouTube Using Topic Modeling, Sentiment Analysis, and Language Analysis

“…In a generic manner, topic modeling is a methodology that comprises different algorithms that identify, comprehend, and annotate the thematic structure in a collection of documents [77]. Topic modeling of the information on the web has had multiple applications related to the investigation of the perception, preparedness, response, views, and opinions of the general public during different virus outbreaks in the recent past, such as MPox [78], human papillomavirus [79], Zika virus [80], Middle East respiratory syndrome [81], dengue [82], and the flu [83]. In summary, the following research gaps exist in relation to misinformation analysis about COVID-19 on YouTube: (a) A lack of focus on topic modeling: Several works in this field [67][68][69][70][71][72][73][74][75][76] have focused on content analysis of YouTube videos.…”

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

“…Additionally, since TextBlob and VADER were designed mainly for English-language texts, they may not be as effective when used in texts in other languages. Therefore, a toolkit for analyzing text sentiments and emotions in a wide range of languages is the Pysentimiento library, which offers support for multiple languages [20][21][22], including Spanish [23,24]. Furthermore, Pysentimiento uses state-of-the-art machine learning models, such as BERT (Bidirectional Encoder Representations from Transformers) models, for sentiment analysis.…”

Health activism, vaccine, and mpox discourse: BERTopic based mixed-method analyses of tweets from sexual minority men and gender diverse (SMMGD) individuals in the U.S.

“…5 Similarly, sexual minority men and gender diverse (SMMGD) individuals have been battling with the so-called “gay disease” stigma attached to HIV/AIDS since the 1980s when the infection and death cases were initially reported in North America to be prevalent among SMMGD. 6 The recent mpox outbreak, coupled with rising misinformation, 7 stigma, 8,9 and conspiracy theories, 10 may, like HIV, experience a stigmatization process that leads to delayed care-seeking and further marginalization of SMMGD individuals.…”

“…Even in a study that did focus on mpox and this community, the analyses were performed on mpox tweets containing keywords about lesbian, gay, bisexual, transgender, queer/questioning, intersex and other sexual or gender identities (LGBTQI+), and the posts were from general users not necessarily self-identified as LGBTQI+. 9 Not specific to mpox, one study 12 found more negative patient experience sentiment among LGBTQI+ users than non-LGBTQI+ users, which demonstrates the importance of considering the post author’s identity when analyzing social media data. This study addresses this research gap by analyzing online mpox posts from a specific user cohort, and discussing implications of our findings for health communication tackling mpox and future disease outbreaks with an emphasis on fairness, equity, as well as stigma prevention and control.…”