Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets

Abstract

In the last decade and a half, the world has experienced outbreaks of a range of viruses such as COVID-19, H1N1, flu, Ebola, Zika virus, Middle East Respiratory Syndrome (MERS), measles, and West Nile virus, just to name a few. During these virus outbreaks, the usage and effectiveness of social media platforms increased significantly, as such platforms served as virtual communities, enabling their users to share and exchange information, news, perspectives, opinions, ideas, and comments related to the outbreaks. Analysis of this Big Data of conversations related to virus outbreaks using concepts of Natural Language Processing such as Topic Modeling has attracted the attention of researchers from different disciplines such as Healthcare, Epidemiology, Data Science, Medicine, and Computer Science. The recent outbreak of the MPox virus has resulted in a tremendous increase in the usage of Twitter. Prior works in this area of research have primarily focused on the sentiment analysis and content analysis of these Tweets, and the few works that have focused on topic modeling have multiple limitations. This paper aims to address this research gap and makes two scientific contributions to this field. First, it presents the results of performing Topic Modeling on 601,432 Tweets about the 2022 Mpox outbreak that were posted on Twitter between 7 May 2022 and 3 March 2023. The results indicate that the conversations on Twitter related to Mpox during this time range may be broadly categorized into four distinct themes—Views and Perspectives about Mpox, Updates on Cases and Investigations about Mpox, Mpox and the LGBTQIA+ Community, and Mpox and COVID-19. Second, the paper presents the findings from the analysis of these Tweets. The results show that the theme that was most popular on Twitter (in terms of the number of Tweets posted) during this time range was Views and Perspectives about Mpox. This was followed by the theme of Mpox and the LGBTQIA+ Community, which was followed by the themes of Mpox and COVID-19 and Updates on Cases and Investigations about Mpox, respectively. Finally, a comparison with related studies in this area of research is also presented to highlight the novelty and significance of this research work.

Investigating Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis

“…Its vast pool of information not only serves to heighten public awareness but also acts as a beacon, illuminating the locations and contexts of outbreaks. This wealth of real-time data from Twitter proves invaluable in shedding light on the multifaceted aspects of a wide range of topics and matters of interest to the scientific community from different disciplines, such as infectious disease outbreaks [57][58][59][60][61], cryptocurrency and stock markets [62,63], public health concerns [64][65][66][67], societal problems [68][69][70][71][72], emerging technologies [73,74], human behavior analysis [75][76][77][78], and humanitarian issues [79][80][81][82][83], as can be seen from several prior works in these fields, which focused on sentiment analysis and other forms of content analysis of Tweets. Following the COVID-19 epidemic, a growing corpus of studies have used Twitter data to analyze public reactions during this global health emergency [84,85].…”

Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis

“…Its vast pool of information not only serves to heighten public awareness but also acts as a beacon, illuminating the locations and contexts of outbreaks. This wealth of real-time data from Twitter proves invaluable in shedding light on the multifaceted aspects of a wide range of topics and matters of interest to the scientific community from different disciplines, such as infectious disease outbreaks [57][58][59][60][61], cryptocurrency and stock markets [62,63], public health concerns [64][65][66][67], societal problems [68][69][70][71][72], emerging technologies [73,74], human behavior analysis [75][76][77][78], and humanitarian issues [79][80][81][82][83], as can be seen from several prior works in these fields, which focused on sentiment analysis and other forms of content analysis of Tweets. Following the COVID-19 epidemic, a growing corpus of studies have used Twitter data to analyze public reactions during this global health emergency [84,85].…”

Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis and Forecasting of Web Behavior

“…During virus outbreaks of the past such as COVID-19 [6][7][8], MPox [9][10][11], Ebola [12][13][14], H1N1 [15][16][17], and MERS [18][19][20], researchers from different disciplines such as Healthcare, Epidemiology, Big Data, Data Analysis, Data Science, and Computer Science have studied and analyzed the underlining web behavior, as web behavior provides insights into the public health needs, interests, motives, concerns, perspectives, and opinions related to virus outbreaks. Furthermore, web behavior analysis related to a virus outbreak has also had several applications related to the real-time surveillance of outbreaks [21], prediction of cases [22], forecasting the behavior of different strains of a virus [23], timely preparation of public health policies [24], better preparedness of healthcare systems [25], identification of the themes of conversations of the general public [26], and timely implementation of public health policies and guidelines [27]. In addition to this, during virus outbreaks of the recent past, for example, COVID-19, such paradigms of information-seeking and sharing behavior on the internet [28][29][30][31][32] led to the development and dissemination of different conspiracy theories which led to a range of reactions, both positive and negative, in the general public [33][34][35].…”