User perceptions of fake news sharing behavior on social media through social networking sites

Daniel Mican, Luigia-Gabriela Sterie, Dan-Andrei Sitar-Tǎut
{"title":"User perceptions of fake news sharing behavior on social media through social networking sites","authors":"Daniel Mican, Luigia-Gabriela Sterie, Dan-Andrei Sitar-Tǎut","doi":"10.24052/bmr/v13nu01/art-13","DOIUrl":null,"url":null,"abstract":"In the age of information technology, online social networks are part of our daily lives and are the main source of obtaining and transmitting information, which can be a blessing or a curse. Although social networks facilitate access to news and information, one issue remains of serious significance, namely the phenomenon of fake news. The short time of spreading fake news in the online social environment is the main cause for concern, and users' attitudes towards fake news can facilitate or reduce its spread. Therefore, the main objective of the current study is to perform an overall analysis of users' perceptions on the behavior and attitudes toward distributing fake news through social networks. To ensure a comprehensive interpretation of the research topic, we analyzed both the reasons behind the behavior of distributing fake news and the active or passive actions that users apply in relation to them. As verifying the authenticity of the source is an essential component of the preventive behavior of fake information distribution, an analysis of the action was performed to verify the credibility of the sources among users. Therefore, the detailed and joint analysis of the above variables gives a note of originality to this study. In addition, the results of the study have significant practical implications for social platforms and are intended to provide a better understanding of how online social network users perceive fake information and interact with it. More specifically, they can be used in the development of predictive models that have the role of automating the identification of fake news in the context of machine learning algorithms and big data.","PeriodicalId":323589,"journal":{"name":"The Business and Management Review","volume":"123 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Business and Management Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24052/bmr/v13nu01/art-13","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In the age of information technology, online social networks are part of our daily lives and are the main source of obtaining and transmitting information, which can be a blessing or a curse. Although social networks facilitate access to news and information, one issue remains of serious significance, namely the phenomenon of fake news. The short time of spreading fake news in the online social environment is the main cause for concern, and users' attitudes towards fake news can facilitate or reduce its spread. Therefore, the main objective of the current study is to perform an overall analysis of users' perceptions on the behavior and attitudes toward distributing fake news through social networks. To ensure a comprehensive interpretation of the research topic, we analyzed both the reasons behind the behavior of distributing fake news and the active or passive actions that users apply in relation to them. As verifying the authenticity of the source is an essential component of the preventive behavior of fake information distribution, an analysis of the action was performed to verify the credibility of the sources among users. Therefore, the detailed and joint analysis of the above variables gives a note of originality to this study. In addition, the results of the study have significant practical implications for social platforms and are intended to provide a better understanding of how online social network users perceive fake information and interact with it. More specifically, they can be used in the development of predictive models that have the role of automating the identification of fake news in the context of machine learning algorithms and big data.
用户通过社交网站对社交媒体上虚假新闻分享行为的感知
在信息技术时代,在线社交网络是我们日常生活的一部分,是获取和传递信息的主要来源,这可能是一件好事,也可能是一件坏事。虽然社交网络为获取新闻和信息提供了便利,但有一个问题仍然很严重,即假新闻现象。假新闻在网络社会环境中传播的时间短是引起关注的主要原因,用户对假新闻的态度可以促进或减少其传播。因此,本研究的主要目的是全面分析用户对通过社交网络传播假新闻的行为和态度的看法。为了确保对研究主题的全面解释,我们分析了传播假新闻行为背后的原因以及用户对此采取的主动或被动行动。由于验证来源的真实性是虚假信息传播预防行为的重要组成部分,因此对用户中验证来源可信度的行为进行了分析。因此,对上述变量进行详细的联合分析,使本研究具有独创性。此外,研究结果对社交平台具有重要的实际意义,旨在更好地了解在线社交网络用户如何感知虚假信息并与之互动。更具体地说,它们可以用于开发预测模型,在机器学习算法和大数据的背景下,这些模型可以自动识别假新闻。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信