Public sentiment on the global outbreak of monkeypox: an unsupervised machine learning analysis of 352,182 twitter posts

Ng, Qin Xiang; Yau, Chun En; Lim, Yu Liang; Wong, L.K.T.; Liew, Tau Ming

doi:10.1016/j.puhe.2022.09.008

Cited by 42 publications

(32 citation statements)

References 7 publications

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“…A few studies have applied social media mining to Mpox. Ng et al [ 52 ] extracted from Twitter a body of 352,182 original tweets containing the terms “monkeypox,” “monkey pox,” or “monkey_pox,” in the English language, from May 6, 2022, to July 23, 2022, using Bidirectional Encoder Representations from Transformers named entity recognition. The authors identified 5 topics clustered into three major themes: (1) safety concerns, (2) sexual and gender minority stigmatization, and (3) a general lack of faith in public institutions.…”

Section: Discussionmentioning

confidence: 99%

“…Even though the abovementioned studies provided rich information and insightful comments regarding the consequences of blaming minority populations for spreading Mpox, as well as suggestions for mitigating these issues, only a few of them have adopted a social media perspective [51][52][53]. Social media is a web-based environment where people can share their thoughts, ideas, and beliefs.…”

Section: Introductionmentioning

confidence: 99%

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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

Nia¹,

Bragazzi²,

Asgary³

et al. 2023

J Med Internet Res

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Nia¹,

Bragazzi²,

Asgary³

et al. 2023

J Med Internet Res

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“…Farahat et al [ 38 ] discussed tweets relating to the monkeypox virus and analyzed a collection of >8000 tweets through topic modeling and sentiment analysis. A study completed by Ng et al also focused on topic modeling and sentiment analysis but used a larger dataset of >350 000 tweets [ 39 ]. A dataset of monkeypox tweets was presented and made available for analysis by Thakur [ 7 ].…”

Section: Discussionmentioning

confidence: 99%

Analyzing an Emerging Pandemic on Twitter: Monkeypox

Cooper

Radunsky

Hanna

et al. 2023

Open Forum Infectious Diseases

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Introduction Social media platforms like Twitter provide important insights into the public’s perceptions of global outbreaks like monkeypox. By analyzing tweets, we aimed to identify public knowledge and opinions on the Monkeypox virus and related public health issues. Methods We analyzed English language tweets using the keyword “monkeypox” from May 01 to July 23, 2022. We reported gender, ethnicity, and race of Twitter users and analyzed tweets to identify predominant sentiment and emotions. We performed topic modeling and compared cohorts of users who self-identify as LGBTQ+ allies versus users who do not, and cohorts identified as “bots” versus humans. Results A total of 48,330 tweets were written by LGBTQ+ self-identified advocates or allies. The mean sentiment score for all tweets was -0.413 (-4 to +4). Negative tweets comprised 39% of tweets. The most common emotions expressed were fear and sadness. Topic modeling identified unique topics among the four cohorts analyzed. Discussion The spread of mis- and disinformation about monkeypox was common in our tweet library. Various conspiracy theories about the origins of monkeypox, its relationship to global economic concerns, and homophobic and racial comments were common. Conversely, many other tweets helped to provide information about monkeypox vaccines, disease symptoms, and prevention methods. Discussion of rising monkeypox case numbers globally was also a large aspect of the conversation. Conclusion We demonstrated that Twitter is an effective means of tracking sentiment about public healthcare issues. We gained insight into a subset of people, self-identified LGBTQ+ allies, who were more affected by monkeypox.

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“…The results showed that LGBTQ+ advocates or allies posted a total of 48,330 tweets, and the average sentiment score for all the tweets was -0.413 on a scale of -4 to +4. Ng et al [92] collected Tweets that contained the phrases "monkeypox," "monkey pox," or "monkey pox" posted on Twitter between May 6, 2022, to July 23, 2022. They used concepts of topic modeling and sentiment analysis to infer characteristics of communication expressed in these Tweets.…”

Section: Recent Work That Focused On Sentiment Analysis Of Tweets Abo...mentioning

confidence: 99%

Analysis of Public Discourse on Twitter involving COVID-19 and MPox: Findings from Sentiment Analysis and Text Analysis

Thakur¹

2023

Preprint

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Mining and analysis of the Big Data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of Tweets related to Ebola, E-Coli, Dengue, Human papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson's, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as "catalysts" for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both these viruses. While there have been a few works published in the last few months that focused on performing sentiment analysis of Tweets related to either COVID-19 or MPox, none of the prior works in this field thus far involved analysis of Tweets focusing on both COVID-19 and MPox at the same time. With an aim to address this research gap, a total of 61,862 Tweets that focused on Mpox and COVID-19 simultaneously, posted between May 7, 2022, to March 3, 2023, were studied to perform sentiment analysis and text analysis. The findings of this study are manifold. First, the results of sentiment analysis show that almost half the Tweets (the actual percentage is 46.88%) had a negative sentiment. It was followed by Tweets that had a positive sentiment (31.97%) and Tweets that had a neutral sentiment (21.14%). Second, this paper presents the top 50 hashtags that were used in these Tweets. Third, it presents the top 100 most frequently used words that are featured in these Tweets. The findings of text analysis show that some of the commonly used words involved directly referring to either or both viruses. In addition to this, the presence of words such as "Polio", "Biden", "Ukraine", "HIV", "climate", and "Ebola" in the list of the top 100 most frequent words indicate that topics of conversations on Twitter in the context of COVID-19 and MPox also included a high level of interest related to other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that involves a comparison of this work with 49 prior works in this field is presented to uphold the scientific contributions and relevance of the same.

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Public sentiment on the global outbreak of monkeypox: an unsupervised machine learning analysis of 352,182 twitter posts

Cited by 42 publications

References 7 publications

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

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

Analyzing an Emerging Pandemic on Twitter: Monkeypox

Analysis of Public Discourse on Twitter involving COVID-19 and MPox: Findings from Sentiment Analysis and Text Analysis

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