Emerging Applications in Web Behavior Mining and Analysis in Preparation for Disease X

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 1525

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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
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Special Issue Information

Dear Colleagues,

In the recent past, several viruses, such as MPox, COVID-19, the plague, Spanish Flu, and Ebola, have rampaged unopposed across different countries, infecting and leading to the demise of people and the destruction of political regimes, affecting various sectors of the global economy, as well as causing financial and psychosocial burdens the likes of which the world has not witnessed in centuries. As a response to this, various organizations and policy-making bodies, on a global scale, have begun investigating approaches to learn from such virus outbreaks with an aim to not repeat the mistakes of the past during future virus outbreaks.

“Disease X” is a placeholder name that was adopted by the World Health Organization (WHO) in February 2018 on their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. The WHO used the placeholder term “Disease X” to make sure that its planning (such as relevant tests, expanded vaccinations, and production capabilities for vaccines) was robust, versatile, and equipped to deal with an unidentified virus. The idea of Disease X, according to Anthony Fauci (the director of the US National Institute of Allergy and Infectious Diseases at that time), was to motivate the WHO’s investigations on entire classes of viruses rather than just specific strains of certain viruses, with an aim to strengthen the WHO’s preparedness for dealing with such outbreaks. Thus, it is crucial to plan and adopt a comprehensive approach to prevent and predict a new pandemic in the future.

In the modern-day Internet of Everything era, massive volumes of user-generated material are uploaded and disseminated on the Internet. These virus outbreaks of the recent past served as “catalysts” for Internet usage which led to the generation of tremendous amounts of Big Data. This Big Data of web behavior was used as a data resource for the investigation of different research questions related to those virus outbreaks, as well as for the development of systems and applications by researchers from different disciplines, such as healthcare, epidemiology, Big Data, Data Analysis, Data Science, Machine Learning, and Natural Language Processing.

As the world prepares for Disease X, this Special Issue invites papers presenting new discoveries, innovative systems, novel applications, theoretical findings, practical solutions, use cases, analytical findings, and results based on studying, analyzing, and interpreting the Big Data on the Internet generated in the context of recent virus outbreaks, including but not limited to COVID-19, MPox, the plague, Spanish Flu, and Ebola, which have the potential to improve the global preparedness and response for Disease X.

Specific topics of interest 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 the identification of conspiracy theories based on the mining and analysis of the web behavior on the Internet generated from various sources, such as social media platforms, online blogs, and websites in the context of recent virus outbreaks. 

Authors are invited to contribute their original and unpublished works. Both research and review papers are welcome. Research papers presenting preliminary and/or proof-of-concept results are also welcome. Authors may also submit extended versions of their conference papers. However, the 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 a cover letter accompanying the paper submission.

Dr. Nirmalya Thakur
Guest Editor

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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. Computation 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 1800 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

  • Disease X
  • COVID-19
  • MPox
  • web behavior
  • big data
  • data mining
  • data analytics
  • data science
  • machine learning
  • artificial intelligence

Published Papers (1 paper)

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Research

29 pages, 6358 KiB  
Article
Investigation of the Misinformation about COVID-19 on YouTube Using Topic Modeling, Sentiment Analysis, and Language Analysis
by Nirmalya Thakur, Shuqi Cui, Victoria Knieling, Karam Khanna and Mingchen Shao
Computation 2024, 12(2), 28; https://doi.org/10.3390/computation12020028 - 06 Feb 2024
Viewed by 1383
Abstract
The work presented in this paper makes multiple scientific contributions with a specific focus on the analysis of misinformation about COVID-19 on YouTube. First, the results of topic modeling performed on the video descriptions of YouTube videos containing misinformation about COVID-19 revealed four [...] Read more.
The work presented in this paper makes multiple scientific contributions with a specific focus on the analysis of misinformation about COVID-19 on YouTube. First, the results of topic modeling performed on the video descriptions of YouTube videos containing misinformation about COVID-19 revealed four distinct themes or focus areas—Promotion and Outreach Efforts, Treatment for COVID-19, Conspiracy Theories Regarding COVID-19, and COVID-19 and Politics. Second, the results of topic-specific sentiment analysis revealed the sentiment associated with each of these themes. For the videos belonging to the theme of Promotion and Outreach Efforts, 45.8% were neutral, 39.8% were positive, and 14.4% were negative. For the videos belonging to the theme of Treatment for COVID-19, 38.113% were positive, 31.343% were neutral, and 30.544% were negative. For the videos belonging to the theme of Conspiracy Theories Regarding COVID-19, 46.9% were positive, 31.0% were neutral, and 22.1% were negative. For the videos belonging to the theme of COVID-19 and Politics, 35.70% were positive, 32.86% were negative, and 31.44% were neutral. Third, topic-specific language analysis was performed to detect the various languages in which the video descriptions for each topic were published on YouTube. This analysis revealed multiple novel insights. For instance, for all the themes, English and Spanish were the most widely used and second most widely used languages, respectively. Fourth, the patterns of sharing these videos on other social media channels, such as Facebook and Twitter, were also investigated. The results revealed that videos containing video descriptions in English were shared the highest number of times on Facebook and Twitter. Finally, correlation analysis was performed by taking into account multiple characteristics of these videos. The results revealed that the correlation between the length of the video title and the number of tweets and the correlation between the length of the video title and the number of Facebook posts were statistically significant. Full article
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