图书情报工作

图书情报工作 >

2021 , Vol. 65 >Issue 9: 70 - 78

DOI: https://doi.org/10.13266/j.issn.0252-3116.2021.09.008

情报研究

社交媒体虚假健康信息特征识别

  • 张帅
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  • 武汉大学信息管理学院 武汉 430072 武汉大学信息资源研究中心 武汉 430072
张帅(ORCID:0000-0002-5792-877X),博士研究生,E-mail:zs09053@163.com。

收稿日期: 2020-11-15

  修回日期: 2021-02-02

  网络出版日期: 2021-06-02

基金资助

本文系湖北文化名家专项基金资助研究成果之一。

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Study on Feature Identification of False Health Information on Social Media

  • Zhang Shuai
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  • School of Information Management, Wuhan University, Wuhan 430072 Center for Studies of Information Resources, Wuhan University, Wuhan 430072

Received date: 2020-11-15

  Revised date: 2021-02-02

  Online published: 2021-06-02

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摘要

[目的/意义] 识别社交媒体虚假健康信息特征，构建社交媒体虚假健康信息特征清单，以期为社交媒体虚假健康信息特征的测度提供一定理论支撑，也为用户和社交媒体平台判别虚假健康信息提供有益参考。[方法/过程] 采集1 004条社交媒体健康数据，利用程序化编码抽取社交媒体虚假健康信息的关键特征，运用卡方检验和方差分析揭示社交媒体虚假健康信息的显著特征，并构建社交媒体虚假健康信息特征清单。[结果/结论] 研究结果表明，社交媒体虚假健康信息特征具有表面特征、语义特征和来源特征3个维度、11个主要特征以及29个子特征。其中，社交媒体上食品安全主题的虚假健康信息在"术语包装"特征上表现得更为显著；"夸大事实"为社交媒体上常见疾病主题虚假健康信息的显著特征；社交媒体上养生保健主题的虚假健康信息具有"元数据缺失"和"假借权威"显著特征。

关键词： 社交媒体; 健康信息; 虚假特征; 特征识别

本文引用格式

张帅 . 社交媒体虚假健康信息特征识别[J]. 图书情报工作, 2021 , 65(9) : 70 -78 . DOI: 10.13266/j.issn.0252-3116.2021.09.008

Abstract

[Purpose/significance] This study identifies the features of false health information on social media and construct a list of false health information characteristics on social media, in order to provide certain theoretical support for the measurement of false health information features on social media, and also provide a useful reference for users and social media service platforms to distinguish false health information.[Method/process] 1 004 pieces of empirical data from social media were collected, and the key features of false health information were extracted by programmatic coding. Then the chi-square test and analysis of variance were adopted to identify significant features of health misinformation. In addition, this study developed a list of features to identify health misinformation on social media.[Result/conclusion] It was shown that the features of false health information on social media had three dimensions:surface features, semantic features, and source features. There were 11 main features and 29 sub-features. It was found that the features of "term packaging" on food safety topic was more notable than other topics; "exaggerated facts" on common diseases topic was more significant than other topics; the features of "lack of meta-information" and "fake authority" on healthcare topic were more prominent than other topics.

Key words： social media; health information; false features; feature identification

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