The Role of Social Media during the Ongoing Outbreaks of COVID-19 and Monkeypox: Applications, Use-Cases, Analytics, and Beyond

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 8997

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Department of Computer Science, Emory University, Atlanta, GA 30322, USA
Interests: big data; data analysis; human-computer interaction; machine learning; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ongoing outbreaks of COVID-19 and monkeypox (mpox) have resulted in people from all over the world using social media platforms for information seeking and sharing, as well as for the communication of views, opinions, feedback, perspectives, and suggestions on a wide range of topics related to these outbreaks, which include policies for reducing the spread of these viruses, treatments, vaccines, school closures, and travel guidelines, just to name a few.

Since the initial cases in December 2019, the SARS-CoV-2 virus has undergone multiple mutations, and as a result, several variants have been detected in different parts of the world. Some of these include Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), Epsilon (B.1.427 and B.1.429), Eta (B.1.525), Iota (B.1.526), Kappa (B.1.617.1), Zeta (P.2), Mu (B.1.621 and B.1.621.1), and Omicron (B.1.1.529, BA.1, BA.1.1, BA.2, BA.3, BA.4, and BA.5) [1]. At present, there have been more than 674,814,341 cases and 6,759,130 deaths on a global scale due to COVID-19 [2].

Monkeypox (mpox) is a re-emerging zoonotic disease. At present, 85,158 cases have been recorded, with 83,872 cases in locations that have not historically reported mpox [3].

These virus outbreaks have served as “catalysts” for social media usage and are resulting in the generation of tremendous amounts of Big Data related to such paradigms of social media behavior. These Big Data can be used as a data resource for the investigation of different research questions, use cases, and applications to advance research and developments in these fields.

This Special Issue invites papers presenting new discoveries, theoretical findings, practical solutions, use cases, analytical findings, novel applications, and results based on studying, analyzing, and interpreting the Big Data on social media platforms generated in the context of the ongoing outbreaks of COVID-19 and monkeypox. Specific topics could include but are not limited to text mining, text classification, text clustering, text categorization, topic modeling, opinion mining, sentiment analysis, aspect-based sentiment analysis, spam detection, fake news tracking, misinformation detection, and identification of conspiracy theories on social media platforms, such as Twitter, Facebook, Instagram, YouTube, etc., with a central focus on COVID-19 or monkeypox (mpox).

Authors are invited to contribute their original and unpublished works. Both research and review papers are welcome. Research papers presenting preliminary and proof-of-concept results are also welcome. Authors may also submit extended versions of their conference papers. However, authors of such papers should make significant improvements/extensions to their conference papers, and the details of these improvements/extensions should be clearly outlined in the cover letter accompanying the paper submission.

References:

[1] CDC, “SARS-CoV-2 variant classifications and definitions,” Centers for Disease Control and Prevention, 29-Aug-2022. Available: https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-classifications.html. [Accessed: 29-Jan-2023].

[2] “COVID live - Coronavirus statistics - worldometer,” Worldometers.info. Available: https://www.worldometers.info/coronavirus/. [Accessed: 29-Jan-2023].

[3] CDC, “2022 mpox outbreak global map,” Centers for Disease Control and Prevention, 27-Jan-2023. Available: https://www.cdc.gov/poxvirus/monkeypox/response/2022/world-map.html. [Accessed: 29-Jan-2023].

Dr. Nirmalya Thakur
Guest Editor

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Keywords

  • COVID-19
  • monkeypox
  • social media
  • Twitter
  • big data
  • data mining
  • data analytics
  • data science
  • machine learning
  • artificial intelligence

Published Papers (4 papers)

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Research

17 pages, 1603 KiB  
Article
Monkeypox, Disinformation, and Fact-Checking: A Review of Ten Iberoamerican Countries in the Context of Public Health Emergency
by Noemí Morejón-Llamas and F. J. Cristòfol
Information 2023, 14(7), 390; https://doi.org/10.3390/info14070390 - 9 Jul 2023
Cited by 2 | Viewed by 1677
Abstract
This paper examines the disinformation and fact-checking activity of ten Ibero-American countries during the outbreak of monkeypox in 2022. Using a mixed-methods approach based on content analysis, the debunkings published by these organizations on their websites between 7 May and 10 September 2022 [...] Read more.
This paper examines the disinformation and fact-checking activity of ten Ibero-American countries during the outbreak of monkeypox in 2022. Using a mixed-methods approach based on content analysis, the debunkings published by these organizations on their websites between 7 May and 10 September 2022 are studied. The countries with the highest number of debunkings are Spain and Bolivia, with two verification agencies, Maldita and Bolivia Verifica. The outbreak’s onset marked a peak in the spread of hoaxes, particularly following the declaration of the disease as a public health emergency. The identification of disinformants is challenging due to the diverse dissemination channels, although Twitter predominantly serves as the platform of choice. The preferred format for disinformation is image text, and the common theme links monkeypox to a side effect of the SARS-CoV-2 vaccine. Furthermore, the internationalization capacity of scientific hoaxes is demonstrated. Fact-checking agencies conduct adequate and thorough source verification, predominantly relying on official and expert sources. However, they employ limited digital tools that could expedite the verification process. Disinformation regarding monkeypox is closely related to COVID-19 hoaxes, either by resurrecting conspiracy theories or through the dissemination of speeches by well-known anti-vaccine activists who belong to healthcare collectives and were influential during the health pandemic. Full article
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21 pages, 1726 KiB  
Article
Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models
by Conor Rosato, Robert E. Moore, Matthew Carter, John Heap, John Harris, Jose Storopoli and Simon Maskell
Information 2023, 14(3), 170; https://doi.org/10.3390/info14030170 - 7 Mar 2023
Viewed by 2008
Abstract
The emergence of the novel coronavirus (COVID-19) generated a need to quickly and accurately assemble up-to-date information related to its spread. In this research article, we propose two methods in which Twitter is useful when modelling the spread of COVID-19: (1) machine learning [...] Read more.
The emergence of the novel coronavirus (COVID-19) generated a need to quickly and accurately assemble up-to-date information related to its spread. In this research article, we propose two methods in which Twitter is useful when modelling the spread of COVID-19: (1) machine learning algorithms trained in English, Spanish, German, Portuguese and Italian are used to identify symptomatic individuals derived from Twitter. Using the geo-location attached to each tweet, we map users to a geographic location to produce a time-series of potential symptomatic individuals. We calibrate an extended SEIRD epidemiological model with combinations of low-latency data feeds, including the symptomatic tweets, with death data and infer the parameters of the model. We then evaluate the usefulness of the data feeds when making predictions of daily deaths in 50 US States, 16 Latin American countries, 2 European countries and 7 NHS (National Health Service) regions in the UK. We show that using symptomatic tweets can result in a 6% and 17% increase in mean squared error accuracy, on average, when predicting COVID-19 deaths in US States and the rest of the world, respectively, compared to using solely death data. (2) Origin/destination (O/D) matrices, for movements between seven NHS regions, are constructed by determining when a user has tweeted twice in a 24 h period in two different locations. We show that increasing and decreasing a social connectivity parameter within an SIR model affects the rate of spread of a disease. Full article
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17 pages, 492 KiB  
Article
Modeling and Moderation of COVID-19 Social Network Chat
by Félix Gélinas-Gascon and Richard Khoury
Information 2023, 14(2), 124; https://doi.org/10.3390/info14020124 - 15 Feb 2023
Cited by 1 | Viewed by 1775
Abstract
Negative social media usage during the COVID-19 pandemic has highlighted the importance of understanding the spread of misinformation and toxicity in public online discussions. In this paper, we propose a novel unsupervised method to discover the structure of online COVID-19-related conversations. Our method [...] Read more.
Negative social media usage during the COVID-19 pandemic has highlighted the importance of understanding the spread of misinformation and toxicity in public online discussions. In this paper, we propose a novel unsupervised method to discover the structure of online COVID-19-related conversations. Our method trains a nine-state Hidden Markov Model (HMM) initialized from a biclustering of 23 features extracted from online messages. We apply our method to 16,000 conversations (1.5 million messages) that took place on the Facebook pages of 15 Canadian newspapers following COVID-19 news items, and show that it can effectively extract the conversation structure and discover the main themes of the messages. Furthermore, we demonstrate how the PageRank algorithm and the conversation graph discovered can be used to simulate the impact of five different moderation strategies, which makes it possible to easily develop and test new strategies to limit the spread of harmful messages. Although our work in this paper focuses on the COVID-19 pandemic, the methodology is general enough to be applied to handle communications during future pandemics and other crises, or to develop better practices for online community moderation in general. Full article
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9 pages, 1697 KiB  
Communication
A Real-Time Infodemiology Study on Public Interest in Mpox (Monkeypox) following the World Health Organization Global Public Health Emergency Declaration
by Akshaya Srikanth Bhagavathula and Jacques E. Raubenheimer
Information 2023, 14(1), 5; https://doi.org/10.3390/info14010005 - 22 Dec 2022
Cited by 6 | Viewed by 1696
Abstract
Google Trends (GT) is a useful real-time surveillance tool for epidemic outbreaks such as monkeypox (Mpox). GT provides hour-by-hour (real-time) data for the last seven days of Google searches. Non-real-time data are a random sample that encompasses search trends from 2004 and up [...] Read more.
Google Trends (GT) is a useful real-time surveillance tool for epidemic outbreaks such as monkeypox (Mpox). GT provides hour-by-hour (real-time) data for the last seven days of Google searches. Non-real-time data are a random sample that encompasses search trends from 2004 and up to 72 h. Google Health Trends (GHT) API extracts daily raw search probabilities relative to the time period and size of the underlying population. However, little is known about the utility of GT real-time surveillance and GHT API following the public health announcements. Thus, this study aimed to analyzed Mpox GT real-time, non-real-time, and GHT API data 72 h before and after the WHO declared Mpox a public health emergency of international concern (PHEIC) in the top five Mpox-affected countries. Joinpoint regression was used to measure hourly percentage changes (HPC) in search volume. The WHO PHEIC statement on Mpox generated 18,225.6 per 10 million Google searches in the U.S. and Germany (946.8), and in 0–4 h, the HPC increased by an average of 103% (95% CI: 37.4–200.0). This study showed the benefits of real-time surveillance and the GHT API for monitoring online demand for information on emerging infectious diseases such as Mpox. Full article
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