Carmen Peñafiel-Saiz, Lázaro Echegaray-Eizaguirre, Amaia Perez-de-Arriluzea-Madariaga
{"title":"The Impact of Biases on Health Disinformation Research","authors":"Carmen Peñafiel-Saiz, Lázaro Echegaray-Eizaguirre, Amaia Perez-de-Arriluzea-Madariaga","doi":"10.3390/soc14050064","DOIUrl":null,"url":null,"abstract":"This work analyses the treatment of elements such as biases and their relationship with disinformation in international academic production. The first step in this process was to carry out a search for papers published in academic journals indexed in the main indexing platforms. This was followed by a bibliometric analysis involving an analysis of the production and impact of the selected publications, using social media techniques and a semantic content analysis based on abstracts. The data obtained from Web of Science, Scopus, and Dimensions, relating to health, biases, and fake news as well as post-truth, show how these works have multiplied in the last decade. The question relating to this research is as follows: How have cognitive biases been treated in national and international academic journals? This question is answered with respect to the scientific or research method. The results, which date from 2000 to 2024, show a considerable academic dedication to exploring the relationship between biases and health disinformation. In all these communities we have observed a relationship between production with the field of medicine as a general theme and social media. Furthermore, this connection is always tied to other subjects, such as an aversion to vaccines in Community 10; disinformation about COVID-19 on social media in Community 5; COVID-19 and conspiracy theories in Community 6; and content for the dissemination of health-related subjects on YouTube and the disinformation spread about them. The community analysis carried out shows a common factor in all the analysed communities—that of cognitive bias.","PeriodicalId":21795,"journal":{"name":"Societies","volume":"55 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Societies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/soc14050064","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SOCIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
This work analyses the treatment of elements such as biases and their relationship with disinformation in international academic production. The first step in this process was to carry out a search for papers published in academic journals indexed in the main indexing platforms. This was followed by a bibliometric analysis involving an analysis of the production and impact of the selected publications, using social media techniques and a semantic content analysis based on abstracts. The data obtained from Web of Science, Scopus, and Dimensions, relating to health, biases, and fake news as well as post-truth, show how these works have multiplied in the last decade. The question relating to this research is as follows: How have cognitive biases been treated in national and international academic journals? This question is answered with respect to the scientific or research method. The results, which date from 2000 to 2024, show a considerable academic dedication to exploring the relationship between biases and health disinformation. In all these communities we have observed a relationship between production with the field of medicine as a general theme and social media. Furthermore, this connection is always tied to other subjects, such as an aversion to vaccines in Community 10; disinformation about COVID-19 on social media in Community 5; COVID-19 and conspiracy theories in Community 6; and content for the dissemination of health-related subjects on YouTube and the disinformation spread about them. The community analysis carried out shows a common factor in all the analysed communities—that of cognitive bias.