Automatic detection of health misinformation: a systematic review
The spread of health misinformation has the potential to cause serious harm to public health, from leading to vaccine hesitancy to adoption of unproven disease treatments. In addition, it could have other effects on society such as an increase in hate speech towards ethnic groups or medical experts....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220340/ https://www.ncbi.nlm.nih.gov/pubmed/37360776 http://dx.doi.org/10.1007/s12652-023-04619-4 |
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author | Schlicht, Ipek Baris Fernandez, Eugenia Chulvi, Berta Rosso, Paolo |
author_facet | Schlicht, Ipek Baris Fernandez, Eugenia Chulvi, Berta Rosso, Paolo |
author_sort | Schlicht, Ipek Baris |
collection | PubMed |
description | The spread of health misinformation has the potential to cause serious harm to public health, from leading to vaccine hesitancy to adoption of unproven disease treatments. In addition, it could have other effects on society such as an increase in hate speech towards ethnic groups or medical experts. To counteract the sheer amount of misinformation, there is a need to use automatic detection methods. In this paper we conduct a systematic review of the computer science literature exploring text mining techniques and machine learning methods to detect health misinformation. To organize the reviewed papers, we propose a taxonomy, examine publicly available datasets, and conduct a content-based analysis to investigate analogies and differences among Covid-19 datasets and datasets related to other health domains. Finally, we describe open challenges and conclude with future directions. |
format | Online Article Text |
id | pubmed-10220340 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-102203402023-05-30 Automatic detection of health misinformation: a systematic review Schlicht, Ipek Baris Fernandez, Eugenia Chulvi, Berta Rosso, Paolo J Ambient Intell Humaniz Comput Original Research The spread of health misinformation has the potential to cause serious harm to public health, from leading to vaccine hesitancy to adoption of unproven disease treatments. In addition, it could have other effects on society such as an increase in hate speech towards ethnic groups or medical experts. To counteract the sheer amount of misinformation, there is a need to use automatic detection methods. In this paper we conduct a systematic review of the computer science literature exploring text mining techniques and machine learning methods to detect health misinformation. To organize the reviewed papers, we propose a taxonomy, examine publicly available datasets, and conduct a content-based analysis to investigate analogies and differences among Covid-19 datasets and datasets related to other health domains. Finally, we describe open challenges and conclude with future directions. Springer Berlin Heidelberg 2023-05-27 /pmc/articles/PMC10220340/ /pubmed/37360776 http://dx.doi.org/10.1007/s12652-023-04619-4 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/) . |
spellingShingle | Original Research Schlicht, Ipek Baris Fernandez, Eugenia Chulvi, Berta Rosso, Paolo Automatic detection of health misinformation: a systematic review |
title | Automatic detection of health misinformation: a systematic review |
title_full | Automatic detection of health misinformation: a systematic review |
title_fullStr | Automatic detection of health misinformation: a systematic review |
title_full_unstemmed | Automatic detection of health misinformation: a systematic review |
title_short | Automatic detection of health misinformation: a systematic review |
title_sort | automatic detection of health misinformation: a systematic review |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220340/ https://www.ncbi.nlm.nih.gov/pubmed/37360776 http://dx.doi.org/10.1007/s12652-023-04619-4 |
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