Special Issue "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: 15 June 2023 | Viewed by 1390

Special Issue Editor

Dr. Nirmalya Thakur
E-Mail Website
Guest Editor
Department of Computer Science, Emory University, Atlanta, GA 30322, USA
Interests: human–computer interaction; big data; artificial intelligence; machine learning; data science; Internet of Things; natural language processing

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (1 paper)

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Research

Communication
A Real-Time Infodemiology Study on Public Interest in Mpox (Monkeypox) following the World Health Organization Global Public Health Emergency Declaration
Information 2023, 14(1), 5; https://doi.org/10.3390/info14010005 - 22 Dec 2022
Cited by 1 | Viewed by 610
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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