Social Media during COVID-19 & MonkeyPox 2023 : SPECIAL ISSUE: The Role of Social Media during the Ongoing Outbreaks of COVID-19 and Monkeypox: Applications, Use-Cases, Analytics, and Beyond
Call For Papers
This is a special issue of Information (ISSN 2078-2489). Information is an international, scientific open-access journal of information science and technology, data, knowledge, and communication, published monthly by MDPI.
Journal Rank: CiteScore - Q2 (Information Systems)
High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, dblp, and other databases. For complete information about the indexing of this journal, please refer to: https://www.mdpi.com/journal/information/indexing
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.9 days after submission; acceptance to publication is undertaken in 3.4 days (median values for papers published in this journal in the first half of 2022).
𝐒𝐏𝐄𝐂𝐈𝐀𝐋 𝐈𝐒𝐒𝐔𝐄 𝐈𝐍𝐅𝐎𝐑𝐌𝐀𝐓𝐈𝐎𝐍
The ongoing outbreaks of COVID-19 and monkeypox 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. 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.
Authors whose works focus on monkeypox are invited to view the MonkeyPox2022Tweets dataset , which is an open access dataset of more than 250,000 Tweets related to the 2022 monkeypox outbreak. Authors whose works focus on COVID-19 are invited to view the Twitter datasets on COVID-19 presented in [2, 3]. The dataset presented in  comprises more than 500,000 Tweets about the Omicron variant of COVID-19 and the dataset presented in  comprises more than 50,000 Tweets about online learning posted during the worldwide surge of the Omicron variant. Submissions based on these datasets are strongly encouraged.
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.
 Thakur, N. MonkeyPox2022Tweets: The First Public Twitter Dataset on the 2022 MonkeyPox Outbreak. Preprints 2022, doi: 10.20944/preprints202206.0172.v3
 Thakur, N.; Han, C.Y. An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection. COVID 2022, 2, 1026–1049, doi:10.3390/covid2080076.
 Thakur, N. A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave. Data 2022, 7, 109, doi:10.3390/data7080109.
𝐌𝐀𝐍𝐔𝐒𝐂𝐑𝐈𝐏𝐓 𝐒𝐔𝐁𝐌𝐈𝐒𝐒𝐈𝐎𝐍 𝐈𝐍𝐅𝐎𝐑𝐌𝐀𝐓𝐈𝐎𝐍
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to https://susy.mdpi.com/user/manuscripts/upload?form[journal_id]=50. 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 at https://www.mdpi.com/journal/information/instructions.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 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.
Discounts on the APC may be available on a case-by-case basis. If you would like to request a discount for the APC for your submission to this Special Issue, please contact the Journal's editorial office at email@example.com
𝐆𝐔𝐄𝐒𝐓 𝐄𝐃𝐈𝐓𝐎𝐑 𝐈𝐍𝐅𝐎𝐑𝐌𝐀𝐓𝐈𝐎𝐍
Dr. Nirmalya Thakur
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; and natural language processing