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Infodemic: Challenges and solutions in topic discovery and data process

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic was a huge shock to society, and the ensuing information problems had a huge impact on society at the same time. The urgent need to understand the Infodemic, i.e., the importance of the spread of false information related to the epidemic,...

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Autores principales: Zhang, Jinjin, Pan, Yang, Lin, Han, Sun, Zhoubao, Wu, Pingping, Tu, Juan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483774/
https://www.ncbi.nlm.nih.gov/pubmed/37679764
http://dx.doi.org/10.1186/s13690-023-01179-z
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author Zhang, Jinjin
Pan, Yang
Lin, Han
Sun, Zhoubao
Wu, Pingping
Tu, Juan
author_facet Zhang, Jinjin
Pan, Yang
Lin, Han
Sun, Zhoubao
Wu, Pingping
Tu, Juan
author_sort Zhang, Jinjin
collection PubMed
description BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic was a huge shock to society, and the ensuing information problems had a huge impact on society at the same time. The urgent need to understand the Infodemic, i.e., the importance of the spread of false information related to the epidemic, has been highlighted. However, while there is a growing interest in this phenomenon, studies on the topic discovery, data collection, and data preparation phases of the information analysis process have been lacking. OBJECTIVE: Since the epidemic is unprecedented and has not ended to this day, we aimed to examine the existing Infodemic-related literature from January 2019 to December 2022. METHODS: We have systematically searched ScienceDirect and IEEE Xplore databases with some search limitations. From the searched literature we selected titles, abstracts and keywords, and limitations sections. We conducted an extensive structured literature search and analysis by filtering the literature and sorting out the available information. RESULTS: A total of 47 papers ended up meeting the requirements of this review. Researchers in all of these literatures encountered different challenges, most of which were focused on the data collection step, with few challenges encountered in the data preparation phase and almost none in the topic discovery section. The challenges were mainly divided into the points of how to collect data quickly, how to get the required data samples, how to filter the data, what to do if the data set is too small, how to pick the right classifier and how to deal with topic drift and diversity. In addition, researchers have proposed partial solutions to the challenges, and we have also proposed possible solutions. CONCLUSIONS: This review found that Infodemic is a rapidly growing research area that attracts the interest of researchers from different disciplines. The number of studies in this field has increased significantly in recent years, with researchers from different countries, including the United States, India, and China. Infodemic topic discovery, data collection, and data preparation are not easy, and each step faces different challenges. While there is some research in this emerging field, there are still many challenges that need to be addressed. These findings highlight the need for more articles to address these issues and fill these gaps.
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spelling pubmed-104837742023-09-08 Infodemic: Challenges and solutions in topic discovery and data process Zhang, Jinjin Pan, Yang Lin, Han Sun, Zhoubao Wu, Pingping Tu, Juan Arch Public Health Research BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic was a huge shock to society, and the ensuing information problems had a huge impact on society at the same time. The urgent need to understand the Infodemic, i.e., the importance of the spread of false information related to the epidemic, has been highlighted. However, while there is a growing interest in this phenomenon, studies on the topic discovery, data collection, and data preparation phases of the information analysis process have been lacking. OBJECTIVE: Since the epidemic is unprecedented and has not ended to this day, we aimed to examine the existing Infodemic-related literature from January 2019 to December 2022. METHODS: We have systematically searched ScienceDirect and IEEE Xplore databases with some search limitations. From the searched literature we selected titles, abstracts and keywords, and limitations sections. We conducted an extensive structured literature search and analysis by filtering the literature and sorting out the available information. RESULTS: A total of 47 papers ended up meeting the requirements of this review. Researchers in all of these literatures encountered different challenges, most of which were focused on the data collection step, with few challenges encountered in the data preparation phase and almost none in the topic discovery section. The challenges were mainly divided into the points of how to collect data quickly, how to get the required data samples, how to filter the data, what to do if the data set is too small, how to pick the right classifier and how to deal with topic drift and diversity. In addition, researchers have proposed partial solutions to the challenges, and we have also proposed possible solutions. CONCLUSIONS: This review found that Infodemic is a rapidly growing research area that attracts the interest of researchers from different disciplines. The number of studies in this field has increased significantly in recent years, with researchers from different countries, including the United States, India, and China. Infodemic topic discovery, data collection, and data preparation are not easy, and each step faces different challenges. While there is some research in this emerging field, there are still many challenges that need to be addressed. These findings highlight the need for more articles to address these issues and fill these gaps. BioMed Central 2023-09-07 /pmc/articles/PMC10483774/ /pubmed/37679764 http://dx.doi.org/10.1186/s13690-023-01179-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhang, Jinjin
Pan, Yang
Lin, Han
Sun, Zhoubao
Wu, Pingping
Tu, Juan
Infodemic: Challenges and solutions in topic discovery and data process
title Infodemic: Challenges and solutions in topic discovery and data process
title_full Infodemic: Challenges and solutions in topic discovery and data process
title_fullStr Infodemic: Challenges and solutions in topic discovery and data process
title_full_unstemmed Infodemic: Challenges and solutions in topic discovery and data process
title_short Infodemic: Challenges and solutions in topic discovery and data process
title_sort infodemic: challenges and solutions in topic discovery and data process
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483774/
https://www.ncbi.nlm.nih.gov/pubmed/37679764
http://dx.doi.org/10.1186/s13690-023-01179-z
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