图书情报工作

图书情报工作 >

2023 , Vol. 67 >Issue 7: 135 - 149

DOI: https://doi.org/10.13266/j.issn.0252-3116.2023.07.012

综述述评

新冠疫情虚假健康信息研究主题与核心要素研究综述

  • 熊回香 ,
  • 孟璇 ,
  • 叶佳鑫
展开
  • 华中师范大学信息管理学院武汉 430079
熊回香,教授,博士,博士生导师;叶佳鑫,博士研究生。

收稿日期: 2022-08-30

  修回日期: 2022-12-07

  网络出版日期: 2023-04-15

基金资助

本文系国家社会科学基金重点项目“数智驱动的在线健康资源挖掘与智慧服务研究”（项目编号：22ATQ004）和2022年度华中师范大学基本科研业务费（人文社科类）交叉科学研究项目“基于量化自我技术的个体健康管理研究”（项目编号：CCNU22JC033）研究成果之一。

收起

A Review of Research Themes and Core Elements of False Health Information Research on COVID-19

  • Xiong Huixiang ,
  • Meng Xuan ,
  • Ye Jiaxin
Expand
  • School of Information Management, Central China Normal University, Wuhan 430079

Received date: 2022-08-30

  Revised date: 2022-12-07

  Online published: 2023-04-15

Fold

摘要

[目的/意义] 系统性回顾和梳理新冠疫情发生以来，国内外关于虚假健康信息的研究内容，以期为相关研究提供参考和借鉴。[方法/过程] 首先，从信息认识论层面出发，确定虚假健康信息的内涵和外延，厘清虚假健康信息内部主流信息形态；其次，通过质性分析总结归纳出虚假健康信息的传播、影响及治理 3 个研究主题，并结合信息、个体主观意识、个体信息行为 3 个核心要素构建虚假健康信息研究的综述框架，在此基础上，依据该框架对相关研究主要内容展开梳理；最后，归纳已有研究成果的不足之处，并从概念内涵深化、研究主题拓展、研究方法融合 3 方面进行展望。[结果/结论] 国内外虚假健康信息的相关研究主要围绕虚假健康信息、个体主观意识、个体行为三大核心要素以及虚假健康信息的传播、影响、治理三大研究主题开展，具有跨学科、研究方法多元、研究视野开阔等特点；虚假健康信息的内部概念边界存在模糊性；研究主题尚有拓展空间，研究方法有待加强融合。

关键词： 虚假健康信息; 概念辨析; 研究综述; 新冠疫情

本文引用格式

熊回香 , 孟璇 , 叶佳鑫 . 新冠疫情虚假健康信息研究主题与核心要素研究综述[J]. 图书情报工作, 2023 , 67(7) : 135 -149 . DOI: 10.13266/j.issn.0252-3116.2023.07.012

Abstract

[Purpose/Significance] This paper systematically reviews and sorts out the research status of false health information at home and abroad since the outbreak of COVID-19, in order to provide references for related research.[Method/Process] First of all, starting from the level of information epistemology, this paper determined the connotation and extension of false health information, and clarified the internal mainstream information form of false health information; secondly, through qualitative analysis, it summarized three research themes of the dissemination, impact and governance of false health information. Combining the three core elements of information, individual subjective consciousness, and individual information behavior, a review framework for research on false health information was constructed. On this basis, the main content of relevant research was sorted out according to this framework; finally, the shortcomings of existing research results were summarized, and it looks forward from three aspects: the deepening of concept connotation, the expansion of research topics, and the integration of research methods.[Result/Conclusion] The related research on false health information at home and abroad is mainly carried out around the three core elements of false health information, individual inner consciousness, and individual behavior, as well as the three major research themes of dissemination, influence and governance of false health information. It has interdisciplinary theories, diverse research methods and broad research horizons.At the same time,the internal conceptual boundaries of false health information are ambiguous, there is still room for expansion of research topics, andresearch methods need to be strengthened and integrated.

Key words： false health information; concept discrimination; literature review; COVID-19 epidemic

参考文献

[1] PATEK A. Diagnostic misinformation at a health resort[J]. Journal of the American Medical Association, 1912, 59(22):1990-1991.
[2] GLASER F. Misinformation about drugs:a problem for drug abuse education[J]. International journal of the addictions, 1970, 5(4):595-609.
[3] BINGER C. Medical information and misinformation[J]. Mental hygiene, 1947, 31(1):1-13.
[4] HUENEMANN R. Combating food misinformation and quackery[J]. Journal of the American Dietetic Association, 1956, 32(7):623-626.
[5] CHANG S K. A model for information exchange. revision[R]. Fort Belvoir:Defense Technical Information Center, 1981.
[6] EBBINGHOUSE C. Medical and legal misinformation on the internet[J]. Searcher, 2000, 8(9):18-27.
[7] MORAHAN-MARTIN J, ANDERSON C. Information and misinformation online:recommendations for facilitating accurate mental health information retrieval and evaluation[J]. Cyberpsychology & behavior, 2000, 3(5):731-746.
[8] ZAROCOSTAS J. How to fight an infodemic[J]. The lancet, 2020, 395(10225):676.
[9] 邓胜利, 孙瑾杰. 图书馆参与虚假健康信息治理的价值、阻滞因素和实现路径[J]. 图书情报工作, 2022, 66(9):14-22.
[10] CALERO VALDEZ A. Human and algorithmic contributions to misinformation online-identifying the culprit[C]//GRIMME C, PREUSS M, TAKES F, et al. Disinformation in open online media. Cham:Springer, 2020:3-15.
[11] 彭知辉. 论中国语境下Disinformation概念的对接、转换与重新阐释[J]. 情报理论与实践, 2022, 45(1):1-10.
[12] 钟义信. 自然语言理解的全信息方法论[J]. 北京邮电大学学报, 2004, 27(4):1-12.
[13] 宋士杰, 赵宇翔, 宋小康, 等. 互联网环境下失真健康信息可信度判断的影响因素研究[J]. 中国图书馆学报, 2019, 45(4):72-85.
[14] 闫慧, 刘畅, 张鹏翼, 等. 信息疫情:信息科学家的观点与对策[J]. 图书情报知识, 2021, 38(1):136-143.
[15] LAZER D, BAUM M, BENKLER Y, et al. The science of fake news[J]. Science, 2018, 359(6380):1094-1096.
[16] DIFONZO N, BORDIA P. Rumor psychology:social and organizational approaches[M]. Washington, DC:American psychological association, 2007.
[17] WORLD HEALTH ORGANIZATION. Novel Coronavirus(2019- nCoV) situation report-13[EB/OL].[2022-07-15]. https://www.who.int/docs/default-source/coronaviruse/situationreports/20200202-sitrep-13-ncov-v3.pdf.
[18] ANDRADE G. Medical conspiracy theories:cognitive science and implications for ethics[J]. Medicine, health care and philosophy, 2020, 23(3):505-518.
[19] ISLAM M S, KAMAL A, KABIR A, et al. COVID-19 vaccine rumors and conspiracy theories:the need for cognitive inoculation against misinformation to improve vaccine adherence[EB/OL].[2022-07-16]. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0251605.
[20] CAO X, SUN J. Exploring the effect of overload on the discontinuous intention of social media users:an SOR perspective[EB/OL].[2022-07-16]. https://www.sciencedirect.com/science/article/abs/pii/S0747563217306738.
[21] MEHRABIAN A, RUSSELL J. An approach to environmental psychology[M]. Cambridge:MIT Press, 1974.
[22] WANG W, CHEN R, OU C, et al. Media or message, which is the king in social commerce? an empirical study of participants' intention to repost marketing messages on social media[EB/OL].[2022-07-16]. https://www.sciencedirect.com/science/article/abs/pii/S0747563218305934.
[23] WU Y, LI E. Marketing mix, customer value, and customer loyalty in social commerce:a stimulus-organism-response perspective[J]. Internet research, 2018, 28(1):74-104.
[24] EROGLU S, MACHLEIT K, DAVIS L. Atmospheric qualities of online retailing:a conceptual model and implications[J]. Journal of business research, 2001, 54(2):177-184.
[25] KIM A, JOHNSON K. Power of consumers using social media:examining the influences of brand-related user-generated content on Facebook[EB/OL].[2022-07-16]. https://www.sciencedirect.com/science/article/abs/pii/S0747563215303186.
[26] DALEY D, KENDALL D. Epidemics and rumours[J]. Nature, 1964, 204(4963):1118.
[27] 陈安, 王子君, 陈樱花. 基于SEIR模型视角的重大公共卫生事件中伪科学网络谣言的传播治理:以新冠肺炎疫情为例[J]. 科技导报, 2020, 38(4):55-65.
[28] 刘倩倩. 不确定环境下的SEIR谣言传播模型及其扩展研究[D]. 乌鲁木齐:新疆大学, 2021.
[29] HAN Q, MIAO F, FAN W. Rumor spreading and monitoring deployment in online social networks[C]//2017 IEEE 17th International conference on communication technology (ICCT). Piscataway:IEEE, 2017:1347-1351.
[30] 阮智慧, 钱爱兵. 突发公共卫生事件中伪健康信息传播的系统动力学模型研究[J]. 医学信息学杂志, 2022, 43(3):18-24.
[31] ZHAO L, WANG J, CHEN Y, et al. SIHR rumor spreading model in social networks[J]. Physica a:statistical mechanics and its applications, 2012, 391(7):2444-2453.
[32] KAUK J, KREYSA H, SCHWEINBERGER S. Understanding and countering the spread of conspiracy theories in social networks:evidence from epidemiological models of twitter data[EB/OL].[2022-07-16]. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0256179.
[33] D'ANDREA V, ARTIME O, CASTALDO N, et al. Epidemic proximity and imitation dynamics drive infodemic waves during the COVID-19 pandemic[EB/OL].[2022-07-18]. https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.4.013158.
[34] 王显芳, 张亮, 张宁. 基于前景理论的微信健康信息质量三方博弈分析[J]. 计算机科学, 2022, 49(S1):694-704.
[35] LIN Y, LEI H, DENG Y. Spread mechanism and influence measurement of online rumors in China during the COVID-19 pandemic[EB/OL].[2022-07-18]. https://arxiv.org/abs/2012.02446.
[36] GRIMES D. Medical disinformation and the unviable nature of COVID-19 conspiracy theories[EB/OL].[2022-07-18]. https://journals.plos.org/plosone/article?id=10.1371/journal. pone.0245900.
[37] INDU V, THAMPI S. A nature-inspired approach based on forest fire model for modeling rumor propagation in social networks[EB/OL].[2022-07-20]. https://www.sciencedirect.com/science/article/abs/pii/S108480451830314X.
[38] AHMED W, VIDAL-ALABALL J, DOWNING J, et al. COVID-19 and the 5G conspiracy theory:social network analysis of Twitter data[EB/OL].[2022-07-20]. https://www.jmir.org/2020/5/e19458.
[39] CHEN E, LERMAN K, FERRARA E. Tracking social media discourse about the COVID-19 pandemic:development of a public coronavirus twitter data set[EB/OL].[2022-07-22]. https://publichealth.jmir.org/2020/2/e19273.
[40] SAFARNEJAD L, XU Q, GE Y, et al. Disinformed social movements and real-information dissemination network structures on social media during a health emergency[J]. American journal of public health, 2020, 110(S3):340-347.
[41] DARIUS P, STEPHANY F. How the far-right polarises Twitter:'highjacking' hashtags in times of COVID-19[EB/OL].[2022-07-22]. https://arxiv.org/abs/2010.05686.
[42] SHAHI G K, DIRKSON A, MAJCHRZAK T. An exploratory study of COVID-19 misinformation on Twitter[EB/OL].[2022- 07-22]. https://www.sciencedirect.com/science/article/pii/S2468696420300458.
[43] NAZAR S, PIETERS T. Plandemic revisited:a product of planned disinformation amplifying the COVID-19"infodemic"[EB/OL].[2022-07-22]. https://www.frontiersin.org/articles/10.3389/fpubh.2021.649930/full.
[44] JEMIELNIAK D, KREMPOVYCH Y. An analysis of AstraZeneca COVID-19 vaccine misinformation and fear mongering on Twitter[EB/OL].[2022-07-23]. https://www.sciencedirect.com/science/article/pii/S0033350621003462.
[45] CHENG M, YIN C, NAZARIAN S, et al. Deciphering the laws of social network-transcendent COVID-19 misinformation dynamics and implications for combating misinformation phenomena[J]. Scientific reports, 2021, 11(1):1-14.
[46] KALANTARI N, LIAO D, MOTTI V. Characterizing the online discourse in Twitter:Users' reaction to misinformation around COVID-19 in Twitter[C]//CHEN Y, LUDWIG H, TU Y, et al. 2021 IEEE International conference on big data (big data). Piscataway:IEEE, 2021:4371-4380.
[47] 王林, 张梦溪, 吴江. 信息生态视角下新冠肺炎疫情的网络舆情传播与演化分析研究[J]. 情报科学, 2022, 40(1):31-37, 50.
[48] RÖCHERT D, SHAHI G, NEUBAUM G, et al. The networked context of COVID-19 misinformation:informational homogeneity on YouTube at the beginning of the pandemic[EB/OL].[2022-07-24]. https://www.sciencedirect.com/science/article/pii/S246869642100046X.
[49] WANG A, LAN J-Y, WANG M-H, et al. The evolution of rumors on a closed social networking platform during COVID-19:algorithm development and content study[EB/OL].[2022-07-24]. https://pubmed.ncbi.nlm.nih.gov/34623954.
[50] DARIUS P, URQUHART M. Disinformed social movements:a large-scale mapping of conspiracy narratives as online harms during the COVID-19 pandemic[EB/OL].[2022-07-24]. https://pubmed.ncbi.nlm.nih.gov/34642647.
[51] MURCHISON C. A history of psychology in autobiography vol.i[M]. Whitefish, MT:Russell & Russell/Atheneum Publishers, 1930.
[52] HAN J W, PARK J H, LEE H N. Effect of exposure to COVID-19 infodemic on infection-preventive intentions among Korean adults[J]. Nursing open, 2022, 9(6):2665-2674.
[53] CHEN L, ZHANG Y, YOUNG R, et al. Effects of vaccine-related conspiracy theories on Chinese young adults' perceptions of the HPV vaccine:an experimental study[J]. Health communication, 2021, 36(11):1343-1353.
[54] ZHOU C, XIU H, WANG Y, et al. Characterizing the dissemination of misinformation on social media in health emergencies:an empirical study based on COVID-19[J]. Information processing & management, 2021, 58(4):102554.
[55] TANDOC J R, LEE J. When viruses and misinformation spread:how young Singaporeans navigated uncertainty in the early stages of the COVID-19 outbreak[J]. New media & society, 2022, 24(3):778-796.
[56] VRDELJA M, VRBOVŠEK S, KLOPČIČ V, et al. Facing the growing COVID-19 infodemic:digital health literacy and information-seeking behaviour of university students in Slovenia[EB/OL].[2022-07-24]. https://www.mdpi.com/1660-4601/18/16/8507.
[57] DING X, ZHANG X, FAN R, et al. Rumor recognition behavior of social media users in emergencies[J]. Journal of management science and engineering, 2022, 7(1):36-47.
[58] 王宁, 李月琳, 牛志鹏, 等. 健康信息规避行为与维持性血液透析患者自我健康管理研究[J]. 情报科学, 2022, 40(9):78-87.
[59] KIM SY, KIM S H. The crisis of public health and infodemic:analyzing belief structure of fake news about COVID-19 pandemic[J]. Sustainability, 2020, 12(23):9904.
[60] HOULDEN S, HODSON J, VELETSIANOS G, et al. The health belief model:how public health can address the misinformation crisis beyond COVID-19[EB/OL].[2022-07-28]. https://www.sciencedirect.com/science/article/pii/S2666535221000768.
[61] EBERHARDT J, LING J. Predicting COVID-19 vaccination intention using protection motivation theory and conspiracy beliefs[J]. Vaccine, 2021, 39(42):6269-6275.
[62] HE L, CHEN Y, XIONG X, et al. Does science literacy guarantee resistance to health rumors? the moderating effect of self-efficacy of science literacy in the relationship between science literacy and rumor belief[EB/OL].[2022-07-28]. https://www.mdpi.com/1660-4601/18/5/2243.
[63] LIU P L, HUANG L. Digital disinformation about COVID-19 and the third-person effect:examining the channel differences and negative emotional outcomes[J]. Cyberpsychology, behavior, and social networking, 2020, 23(11):789-793.
[64] HUSAIN F, SHAHNAWAZ M, KHAN N, et al. Intention to get COVID-19 vaccines:Exploring the role of attitudes, subjective norms, perceived behavioral control, belief in COVID-19 misinformation, and vaccine confidence in Northern India[J]. Human vaccines & immunotherapeutics, 2021, 17(11):3941-3953.
[65] SCANNELL D, DESENS L, GUADAGNO M, et al. COVID-19 vaccine discourse on Twitter:a content analysis of persuasion techniques, sentiment and mis/disinformation[J]. Journal of health communication, 2021, 26(7):443-459.
[66] WOLFE C, EYLEM A, DANDIGNAC M, et al. Understanding the landscape of web-based medical misinformation about vaccination[EB/OL].[2022-07-31]. https://link.springer.com/article/10.3758/s13428-022-01840-5.
[67] 梁晨, 刘紫英. 事实核查与横向阅读:网络谣言治理的新思路[J]. 新媒体研究, 2022, 8(7):39-41, 73.
[68] STEWART E. Detecting fake news:two problems for content moderation[J]. Philosophy & technology, 2021, 34(4):923-940.
[69] ROITERO K, SOPRANO M, PORTELLI B, etal. The COVID-19 infodemic:can the crowd judge recent misinformation objectively?[C]//D'AQUIN M, DIETZE S, HAUFF C, et al. Proceedings of the 29th ACM international conference on information & knowledge management. New York:ACM, 2020:1305-1314.
[70] ARMITAGE L, LAWSON B, WHELAN M, et al. Paying SPECIAL consideration to the digital sharing of information during the COVID-19 pandemic and beyond[EB/OL].[2022- 07-31]. https://bjgpopen.org/content/4/2/bjgpopen20x101072. abstract.
[71] LÓPEZ-GARCÍA X, COSTA-SÁNCHEZ C, VIZOSO Á. Journalistic fact-checking of information in pandemic:stakeholders, hoaxes, and strategies to fight disinformation during the COVID-19 crisis in Spain[EB/OL].[2022-08-01]. https://www.mdpi.com/1660-4601/18/3/1227.
[72] COTTER K, DECOOK J, KANTHAWALA S. Fact-checking the crisis:COVID-19, infodemics, and the platformization of truth[EB/OL].[2022-08-01]. https://journals.sagepub.com/doi/pdf/10.1177/20563051211069048.
[73] CHOU W Y, GAYSYNSKY A, VANDERPOOL R. The COVID-19 misinfodemic:moving beyond fact-checking[J]. Health education & behavior, 2021, 48(1):9-13.
[74] NYHAN B, REIFLER J. When corrections fail:The persistence of political misperceptions[J]. Political behavior, 2010, 32(2):303-330.
[75] BODE L, VRAGA E. In related news, that was wrong:the correction of misinformation through related stories functionality in social media[J]. Journal of communication, 2015, 65(4):619-638.
[76] MOHDEB D, LAIFA M, NAIDJA M. An Arabic corpus for COVID-19 related fake news[C]//2021 International conference on recent advances in mathematics and informatics (ICRAMI). Piscataway:IEEE, 2021:1-5.
[77] MURIC G, WU Y, FERRARA E. COVID-19 vaccine hesitancy on social media:building a public twitter data set of antivaccine content, vaccine misinformation, and conspiracies[EB/OL].[2022-08-01]. https://publichealth.jmir.org/2021/11/e30642.
[78] SAFARNEJAD L, XU Q, GE Y, et al. A multiple feature category data mining and machine learning approach to characterize and detect health misinformation on social media[J]. IEEE Internet computing, 2021, 25(5):43-51.
[79] ANOOP K, DEEPAK P, LAJISH V. Emotion cognizance improves health fake news identification[C]//DESAI B, CHO W-S. 24th international database engineering & applications symposium. New York:ACM, 2020:1-10.
[80] ZHAO Y, DA J, YAN J. Detecting health misinformation in online health communities:incorporating behavioral features into machine learning based approaches[EB/OL].[2022-08- 02]. https://www.sciencedirect.com/science/article/abs/pii/S0306457320308852.
[81] RAJU R, BHANDARI S, MOHAMUD S, et al. Transfer learning model for disrupting misinformation during a COVID-19 pandemic[C]//2021 IEEE 11th annual computing and communication workshop and conference (CCWC). Piscataway:IEEE, 2021:245-250.
[82] SICILIA R, FRANCINI L, SODA P. Representation and knowledge transfer for health-related rumour detection[C]//2021 IEEE 34th International symposium on computer-based medical systems (CBMS). Piscataway:IEEE, 2021:591-596.
[83] 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.
[84] WANG X, CHAO F, YU G, et al. Factors influencing fake news rebuttal acceptance during the COVID-19 pandemic and the moderating effect of cognitive ability[EB/OL].[2022- 08-02]. https://www.sciencedirect.com/science/article/pii/S0747563221004970.
[85] MALLA S, ALPHONSE P. Fake or realnews about COVID-19? pretrained transformer model to detect potential misleading news[EB/OL].[2022-08-02]. https://link.springer.com/article/10.1140/epjs/s11734-022-00436-6?utm_campaign=HSCR_JRNLS_AWA1_GL_MPAS_TRDMD&utm_term=null&utm_content=paid&utm_source=trendmd&utm_medium=cpc.
[86] PRANESH R, FAROKHNEJAD M, SHEKHAR A, et al. CMTA:COVID-19 misinformation multilingual analysis on Twitter[C]//KABBARA J, LIN H, PAULLADA A, et al. Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing:student research workshop. Stroudsburg:ACL, 2021:270-283.
[87] DU J, PRESTON S, SUN H, et al. Using machine learning- based approaches for the detection and classification of human papillomavirus vaccine misinformation:infodemiology study of reddit discussions[EB/OL].[2022-08-03]. https://www.jmir.org/2021/8/e26478.
[88] NAKOV P, ALAM F, SHAAR S, et al. A second pandemic? analysis of fake news about COVID-19 vaccines in Qatar[EB/OL].[2022-08-03]. https://arxiv.org/abs/2109.11372.
[89] MAHLOUS A R, AL-LAITH A. Fake news detection in Arabic tweets during the COVID-19 pandemic[EB/OL].[2022-08-03]. https://www.researchgate.net/profile/AliAl-Laith/publication/353068250_Fake_News_Detection_in_Arabic_Tweets_during_the_COVID-19_Pandemic/links/60e67ff10fbf460db8edbd66/Fake-News-Detection-inArabic-Tweets-during-the-COVID-19-Pandemic.pdf.
[90] BARVE Y, SAINI J. Healthcare misinformation detection and fact-checking:a novel approach[EB/OL].[2022-08- 03]. https://www.researchgate.net/profile/JatinderkumarSaini/publication/356013604_Healthcare_Misinformation_Detection_and_Fact-Checking_A_Novel_Approach/links/61c9a3cdd4500608166e8509/Healthcare-MisinformationDetection-and-Fact-Checking-A-Novel-Approach.pdf.
[91] TASHTOUSH Y, ALRABABAH B, DARWISH O, et al. A deep learning framework for detection of COVID-19 fake news on social media platforms[EB/OL].[2022-08-04]. https://www. mdpi.com/2306-5729/7/5/65.
[92] AL-SAREM M, ALSAEEDI A, SAEED F, et al. A novel hybrid deep learning model for detecting COVID-19-related rumors on social media based on LSTM and concatenated parallel CNNs[EB/OL].[2022-08-04]. https://www.mdpi.com/2076-3417/11/17/7940.
[93] DU J, DOU Y, XIA C, et al. Cross-lingual COVID-19 fake news detection[C]//2021 International conference on data mining workshops (ICDMW). Piscataway:IEEE, 2021:859-862.
[94] IWENDI C, MOHAN S, IBEKE E, et al. COVID-19 fake news sentiment analysis[EB/OL].[2022-08-04]. https://www.sciencedirect.com/science/article/pii/S0045790622002439.
[95] FERNÁNDEZ-PICHEL M, LOSADA D, PICHEL J, et al. Comparing traditional and neural approaches for detecting health-related misinformation[C]//Experimental IR meets multilinguality, multimodality, and interaction. CLEF 2021. lecture notes in computer science. Cham:Springer, 2021:78-90.
[96] HONG K, PARK N, HEO S, et al. Effect of e-health literacy on COVID-19 infection-preventive behaviors of undergraduate students majoring in healthcare[EB/OL].[2022-08-03]. https://www.mdpi.com/2227-9032/9/5/573.
[97] HUI H, ZHOU C, LÜ X, et al. Spread mechanism and control strategy of social network rumors under the influence of COVID- 19[J]. Nonlinear dynamics, 2020, 101(3):1933-1949.
[98] LIU T, XIAO X. A framework of AI-based approaches to improving ehealth literacy and combating infodemic[EB/OL].[2022-08-04]. https://www.frontiersin.org/articles/10.3389/fpubh.2021.755808/full.
[99] 黄如花. 从重大突发公共卫生事件的应对谈信息素养教 育的 迫切 性[EB/OL].[2022-07-15]. http://m.people.cn/n4/2020/0302/c655-13739587.html.
[100] WALTER N, BROOKS J, SAUCIER C, et al. Evaluating the impact of attempts to correct health misinformation on social media:a metaanalysis[J]. Health communication, 2021, 36(13):1776-1784.
[101] YOUSUF H, VAN DER LINDEN S, BREDIUS L, et al. A media intervention applying debunking versus non-debunking content to combat vaccine misinformation in elderly in the Netherlands:a digital randomised trial[EB/OL].[2022-08-05]. https://www.sciencedirect.com/science/article/pii/S2589537021001619.
[102] WANG Y. Debunking misinformation about genetically modified food safety on social media:can heuristic cues mitigate biased assimilation?[J]. Science communication, 2021, 43(4):460-485.
[103] ROITERO K, SOPRANO M, PORTELLI B, et al. The COVID-19 infodemic:can the crowd judge recent misinformation objectively?[C]//D'AQUIN M, DIETZE S, HAUFF C, et al. Proceedings of the 29th ACM international conference on information & knowledge management. New York:ACM, 2020:1305-1314.
[104] VIJAYKUMAR S, ROGERSON D, JIN Y, et al. Dynamics of social corrections to peers sharing COVID-19 misinformation on WhatsApp in Brazil[J]. Journal of the American Medical Informatics Association, 2022, 29(1):33-42.
[105] TEODORO D, FERDOWSI S, BORISSOV N, et al. Information retrieval in an infodemic:the case of COVID-19 publications[EB/OL].[2022-08-05]. https://www.jmir.org/2021/9/e30161.
[106] FRÖBE M, GÜNTHER S, BONDARENKO A, et al. Using keyqueries to reduce misinformation in health-related search results[EB/OL].[2022-08-05]. https://webis.de/downloads/publications/papers/froebe_2022c.pdf.
[107] 宋士杰, 赵宇翔, 朱庆华.iField视域下的信息可信度研究:概念溯源、主题演化与未来展望[J]. 中国图书馆学报, 2022, 48(1):107-126.
Options
文章导航

/

〈 〉