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Research on Health Disinformation on the Internet |
Zhu Qinghua1, Chen Qiong1, Lu Dongmei1, Wang Lei1, Song Shijie2, Zhao Yuxiang1, Zhao Yuehua1 |
1.School of Information Management, Nanjing University, Nanjing 210023 2.Business School, Hohai University, Nanjing 211100 |
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Abstract Social media platforms, such as Weibo and WeChat, have become the main channels for health information dissemination on the Internet. Health disinformation has been increasingly attracting the attention of scholars. The purpose of this paper is to promote collaborative governance against health disinformation dissemination mechanisms and contribute to the optimization of the online health information ecosystem through a literature review. The state of the current research on health disinformation is outlined from the concept and connotation perspectives, and the research conducted on the factors influencing health disinformation is reviewed, thereby identifying health disinformation intervention mechanisms. Finally, the research gaps are summarized, and future directions are suggested to preclude health disinformation.
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Received: 19 August 2022
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1 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. 2 李月琳, 张秀, 王姗姗. 社交媒体健康信息质量研究: 基于真伪健康信息特征的分析[J]. 情报学报, 2018, 37(3): 294-304. 3 人民网-舆情频道. 非常时刻别被谣言击中! 分类辟谣指南来了![EB/OL]. (2020-02-03) [2020-02-22]. http://sd.people.com.cn/n2/2020/0203/c373025-33759323.html. 4 Vosoughi S, Roy D, Aral S. The spread of true and false news online[J]. Science, 2018, 359(6380): 1146-1151. 5 Swire-Thompson B, Lazer D. Public health and online misinformation: challenges and recommendations[J]. Annual Review of Public Health, 2020, 41: 433-451. 6 维基百科. 社交媒体[EB/OL]. [2020-02-09]. https://zh.wikipedia.org/wiki/%E7%A4%BE%E4%BC%9A%E5%8C%96%E5%AA%92%E4%BD%93. 7 Krishna A, Thompson T L. Misinformation about health: a review of health communication and misinformation scholarship[J]. American Behavioral Scientist, 2019, 65(2): 316-332. 8 Nyhan B, Reifler J. When corrections fail: the persistence of political misperceptions[J]. Political Behavior, 2010, 32(2): 303-330. 9 Wardle C, Derakhshan H. Information disorder: toward an interdisciplinary framework for research and policy making[R]. Brussels: Council of Europe, 2017: 1-107. 10 Wang Y X, McKee M, Torbica A, et al. Systematic literature review on the spread of health-related misinformation on social media[J]. Social Science & Medicine, 2019, 240: 112552. 11 Peterson W A, Gist N P. Rumor and public opinion[J]. American Journal of Sociology, 1951, 57(2): 159-167. 12 巢乃鹏, 黄娴. 网络传播中的“谣言”现象研究[J]. 情报理论与实践, 2004, 27(6): 586-589, 575. 13 Loftus E F, Hoffman H G. Misinformation and memory: the creation of new memories[J]. Journal of Experimental Psychology: General, 1989, 118(1): 100-104. 14 Ho K K W, Chan J Y, Chiu D K W. Fake news and misinformation during the pandemic: what we know and what we do not know[J]. IT Professional, 2022, 24(2): 19-24. 15 Suarez-Lledo V, Alvarez-Galvez J. Assessing the role of social bots during the COVID-19 pandemic: infodemic, disagreement and criticism[J]. Journal of Medical Internet Research, 2022, 24(8): e36085. 16 Ruiz-Nú?ez C, Segado-Fernández S, Jiménez-Gómez B, et al. Bots’ activity on COVID-19 pro and anti-vaccination networks: analysis of Spanish-written messages on Twitter[J]. Vaccines, 2022, 10(8): 1240. 17 Swire-Thompson B, Lazer D. Reducing health misinformation in science: a call to arms[J]. The ANNALS of the American Academy of Political and Social Science, 2022, 700(1): 124-135. 18 Ramsbottom A, van Schalkwyk M C I, Carters-White L, et al. Food as harm reduction during a drinking session: reducing the harm or normalising harmful use of alcohol? A qualitative comparative analysis of alcohol industry and non-alcohol industry-funded guidance[J]. Harm Reduction Journal, 2022, 19(1): 66. 19 Zhao Y H, Da J W, Yan J Q. Detecting health misinformation in online health communities: incorporating behavioral features into machine learning based approaches[J]. Information Processing & Management, 2021, 58(1): 102390. 20 Zhang S, Ma F C, Liu Y M, et al. Identifying features of health misinformation on social media sites: an exploratory analysis[J]. Library Hi Tech, 2022, 40(5): 1384-1401. 21 Zheng X A, Wu S W, Nie D. Online health misinformation and corrective messages in China: a comparison of message features[J]. Communication Studies, 2021, 72(3): 474-489. 22 Xu Z, Guo H. Using text mining to compare online pro- and anti-vaccine headlines: word usage, sentiments, and online popularity[J]. Communication Studies, 2018, 69(1): 103-122. 23 Greer J, Fitzgerald K, Vijaykumar S. Narrative elaboration makes misinformation and corrective information regarding COVID-19 more believable[J]. BMC Research Notes, 2022, 15(1): Article No.235. 24 Desai A N, Ruidera D, Steinbrink J M, et al. Misinformation and disinformation: the potential disadvantages of social media in infectious disease and how to combat them[J]. Clinical Infectious Diseases, 2022, 74(suppl_3): e34-e39. 25 Warner E L, Barbati J L, Duncan K L, et al. Vaccine misinformation types and properties in Russian troll tweets[J]. Vaccine, 2022, 40(6): 953-960. 26 Quinn E K, Fazel S S, Peters C E. The instagram infodemic: cobranding of conspiracy theories, coronavirus disease 2019 and authority-questioning beliefs[J]. Cyberpsychology, Behavior and Social Networking, 2021, 24(8): 573-577. 27 Teplinsky E, Ponce S B, Drake E K, et al. Online medical misinformation in cancer: distinguishing fact from fiction[J]. JCO Oncology Practice, 2022, 18(8): 584-589. 28 Wilner T, Holton A. Breast cancer prevention and treatment: misinformation on pinterest, 2018[J]. American Journal of Public Health, 2020, 110(S3): S300-S304. 29 Chen K L, Luo Y N, Hu A Y, et al. Characteristics of misinformation spreading on social media during the COVID-19 outbreak in China: a descriptive analysis[J]. Risk Management and Healthcare Policy, 2021, 14: 1869-1879. 30 Broniatowski D A, Kerchner D, Farooq F, et al. Twitter and Facebook posts about COVID-19 are less likely to spread misinformation compared to other health topics[J]. PLoS One, 2022, 17(1): e0261768. 31 Heley K, Gaysynsky A, King A J. Missing the bigger picture: the need for more research on visual health misinformation[J]. Science Communication, 2022, 44(4): 514-527. 32 Loeb S, Sengupta S, Butaney M, et al. Dissemination of misinformative and biased information about prostate cancer on YouTube[J]. European Urology, 2019, 75(4): 564-567. 33 Zhou J, Xiang H L, Xie B J. Better safe than sorry: a study on older adults’ credibility judgments and spreading of health misinformation[J]. Universal Access in the Information Society, 2023, 22: 957-966. 34 Pickles K, Cvejic E, Nickel B, et al. COVID-19 misinformation trends in Australia: prospective longitudinal national survey[J]. Journal of Medical Internet Research, 2021, 23(1): e23805. 35 Chong S K, Ali S H, Doàn L N, et al. Social media use and misinformation among Asian Americans during COVID-19[J]. Frontiers in Public Health, 2022, 9: 764681. 36 Baines A, Seo H, Ittefaq M, et al. Race/ethnicity, online information and COVID-19 vaccination: study of minority immigrants’ Internet use for health-related information[J]. Convergence, 2023, 29(2): 268-287. 37 Stewart R, Madonsela A, Tshabalala N, et al. The importance of social media users’ responses in tackling digital COVID-19 misinformation in Africa[J]. Digital Health, 2022, 8. DOI: 10.1177/20552076221085070. 38 Nielsen-Bohlman L, Panzer A M, Kindig D A. Health literacy: a prescription to end confusion[M]. Washington, D.C.: National Academies Press, 2004. 39 Wang Y. Systematic review on the social mechanism of health misinformation dissemination in the Internet era[J]. European Journal of Public Health, 2018, 28(suppl_4): cky213. 194. 40 Scherer L D, McPhetres J, Pennycook G, et al. Who is susceptible to online health misinformation? A test of four psychosocial hypotheses[J]. Health Psychology, 2021, 40(4): 274-284. 41 McMillan S J, Macias W. Strengthening the safety net for online seniors: factors influencing differences in health information seeking among older Internet users[J]. Journal of Health Communication, 2008, 13(8): 778-792. 42 Ma T J, Atkin D. User generated content and credibility evaluation of online health information: a meta analytic study[J]. Telematics and Informatics, 2017, 34(5): 472-486. 43 Berriche M, Altay S. Internet users engage more with phatic posts than with health misinformation on Facebook[J]. Palgrave Communications, 2020, 6: Article No.71. 44 Pan W J, Liu D Y, Fang J. An examination of factors contributing to the acceptance of online health misinformation[J]. Frontiers in Psychology, 2021, 12: 630268. 45 Chou W Y S, Gaysynsky A, Vanderpool R C. The COVID-19 misinfodemic: moving beyond fact-checking[J]. Health Education & Behavior, 2021, 48(1): 9-13. 46 Jiang S H. The roles of worry, social media information overload, and social media fatigue in hindering health fact-checking[J]. Social Media + Society, 2022, 8(3). DOI: 10.1177/20563051221113070. 47 Jiang S H, Liu P L, Ngien A, et al. The effects of worry, risk perception, information-seeking experience, and trust in misinformation on COVID-19 fact-checking: a survey study in China[J]. Chinese Journal of Communication, 2023, 16(2): 132-149. 48 Sui Y J, Zhang B. Determinants of the perceived credibility of rebuttals concerning health misinformation[J]. International Journal of Environmental Research and Public Health, 2021, 18(3): 1345. 49 Wu Y Y, Kuru O, Campbell S W, et al. Explaining health misinformation belief through news, social, and alternative health media use: the moderating roles of need for cognition and faith in intuition[J]. Health Communication, 2023, 38(7): 1416-1429. 50 Keselman A, Browne A C, Kaufman D R. Consumer health information seeking as hypothesis testing[J]. Journal of the American Medical Informatics Association, 2008, 15(4): 484-495. 51 Seo H, Faris R. Special section on comparative approaches to mis/disinformation: introduction[J]. International Journal of Communication, 2021, 15: 1165-1172. 52 Tang L, Fujimoto K, Amith M T, et al. “Down the rabbit hole” of vaccine misinformation on YouTube: network exposure study[J]. Journal of Medical Internet Research, 2021, 23(1): e23262. 53 Shin J, Valente T. Algorithms and health misinformation: a case study of vaccine books on Amazon[J]. Journal of Health Communication, 2020, 25(5): 394-401. 54 Chou W Y S, Oh A, Klein W M P. Addressing health-related misinformation on social media[J]. The Journal of the American Medical Association, 2018, 320(23): 2417-2418. 55 Safarnejad L, Xu Q, Ge Y R, 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. 56 Safarnejad L, Xu Q, Ge Y R, et al. Contrasting misinformation and real-information dissemination network structures on social media during a health emergency[J]. American Journal of Public Health, 2020, 110(S3): S340-S347. 57 Del Vicario M, Bessi A, Zollo F, et al. The spreading of misinformation online[J]. Proceedings of the National Academy of Sciences of the United States of America, 2016, 113(3): 554-559. 58 Seymour B, Getman R, Saraf A, et al. When advocacy obscures accuracy online: digital pandemics of public health misinformation through an antifluoride case study[J]. American Journal of Public Health, 2015, 105(3): 517-523. 59 Nazar S, Pieters T. Plandemic revisited: a product of planned disinformation amplifying the COVID-19 “infodemic”[J]. Frontiers in Public Health, 2021, 9: 649930. 60 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. 61 Greenhalgh T. How to read a paper: the basics of evidence-based medicine[M]. Hoboken: John Wiley & Sons, 2014. 62 Samuel H W, Za?ane O R. MedFact: towards improving veracity of medical information in social media using applied machine learning[C]// Proceedings of the 31st Canadian Conference on Artificial Intelligence. Cham: Springer, 2018: 108-120. 63 Park M. HealthTrust: assessing the trustworthiness of healthcare information on the Internet[D]. Lawrence: University of Kansas, 2013. 64 Yang F, Liu Y, Yu X H, et al. Automatic detection of rumor on Sina Weibo[C]// Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics. New York: ACM Press, 2012: Article No.13. 65 Jin Z W, Cao J, Zhang Y D, et al. News verification by exploiting conflicting social viewpoints in microblogs[C]// Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2016: 2972-2978. 66 Acemoglu D, Ozdaglar A, ParandehGheibi A. Spread of (mis)information in social networks[J]. Games and Economic Behavior, 2010, 70(2): 194-227. 67 Sicilia R, Giudice S L, Pei Y L, et al. Health-related rumour detection on Twitter[C]// Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Piscataway: IEEE, 2017: 1599-1606. 68 Kinsora A, Barron K, Mei Q Z, et al. Creating a labeled dataset for medical misinformation in health forums[C]// Proceedings of the 2017 IEEE International Conference on Healthcare Informatics. Piscataway: IEEE, 2017: 456-461. 69 Ghenai A, Mejova Y. Catching zika fever: application of crowdsourcing and machine learning for tracking health misinformation on Twitter[C]// Proceedings of the 2017 IEEE International Conference on Healthcare Informatics. Piscataway: IEEE, 2017: 518. 70 Deb A, Majmundar A, Seo S, et al. Social bots for online public health interventions[C]// Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Piscataway: IEEE, 2018: 186-189. 71 Sicilia R, Lo Giudice S, Pei Y L, et al. Twitter rumour detection in the health domain[J]. Expert Systems with Applications, 2018, 110: 33-40. 72 Liu Y, Yu K, Wu X F, et al. Analysis and detection of health-related misinformation on Chinese social media[J]. IEEE Access, 2019, 7: 154480-154489. 73 Li J X. Detecting false information in medical and healthcare domains: a text mining approach[C]// Proceedings of the 7th International Conference on Smart Health. Cham: Springer, 2019: 236-246. 74 Hou R, Pérez-Rosas V, Loeb S, et al. Towards automatic detection of misinformation in online medical videos[C]// Proceedings of the 2019 International Conference on Multimodal Interaction. New York: ACM Press, 2019: 235-243. 75 Choudrie J, Banerjee S, Kotecha K, et al. Machine learning techniques and older adults processing of online information and misinformation: a COVID 19 study[J]. Computers in Human Behavior, 2021, 119: 106716. 76 Afsana F, Kabir M A, Hassan N, et al. Automatically assessing quality of online health articles[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 25(2): 591-601. 77 Du J C, Preston S, Sun H X, et al. Using machine learning-based approaches for the detection and classification of human papillomavirus vaccine misinformation: infodemiology study of reddit discussions[J]. Journal of Medical Internet Research, 2021, 23(8): e26478. 78 Endo P T, Santos G L, de Lima Xavier M E, et al. Illusion of truth: analysing and classifying COVID-19 fake news in Brazilian Portuguese language[J]. Big Data and Cognitive Computing, 2022, 6(2): 36. 79 Upadhyay R, Pasi G, Viviani M. Vec4Cred: a model for health misinformation detection in web pages[J]. Multimedia Tools and Applications, 2023, 82(4): 5271-5290. 80 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. 81 Li Y L, Zhang X, Wang S S. Fake vs. real health information in social media in China[J]. Proceedings of the Association for Information Science and Technology, 2017, 54(1): 742-743. 82 Di Sotto S, Viviani M. Health misinformation detection in the social web: an overview and a data science approach[J]. International Journal of Environmental Research and Public Health, 2022, 19(4): 2173. 83 Song S J, Zhang Y, Yu B. Interventions to support consumer evaluation of online health information credibility: a scoping review[J]. International Journal of Medical Informatics, 2021, 145: 104321. 84 Shah Z, Surian D, Dyda A, et al. Automatically appraising the credibility of vaccine-related web pages shared on social media: a Twitter surveillance study[J]. Journal of Medical Internet Research, 2019, 21(11): e14007. 85 Ghenai A, Mejova Y. Fake cures: user-centric modeling of health misinformation in social media[J]. Proceedings of the ACM on Human-Computer Interaction, 2018, 2(CSCW): Article No.58. 86 朱宏淼, 齐佳音, 靳祯, 等. 医联网环境下失真健康信息传播动力学模型与干预策略研究[J]. 系统工程理论与实践, 2022, 42(7): 1927-1940. 87 Shams A B, Hoque Apu E, Rahman A, et al. Web search engine misinformation notifier extension (SEMiNExt): a machine learning based approach during COVID-19 pandemic[J]. Healthcare, 2021, 9(2): 156. 88 Pandey R, Gautam V, Pal R, et al. A machine learning application for raising WASH awareness in the times of COVID-19 pandemic[J]. Scientific Reports, 2022, 12: Article No.810. 89 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. 90 Bin Naeem S, Kamel Boulos M N. COVID-19 misinformation online and health literacy: a brief overview[J]. International Journal of Environmental Research and Public Health, 2021, 18(15): 8091. 91 Armstrong-Heimsoth A, Johnson M L, McCulley A, et al. Good Googling: a consumer health literacy program empowering parents to find quality health information online[J]. Journal of Consumer Health on the Internet, 2017, 21(2): 111-124. 92 邓胜利, 蔡芸娜. 国外高校图书馆参与虚假健康信息治理调研及启示[J]. 图书情报工作, 2022, 66(9): 23-32. 93 黄雨婷, 冯婕. 信息素养视域下的虚假信息甄别: 国际进展与我国对策[J]. 图书情报知识, 2021(2): 121-132. 94 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. 95 Mitsuhashi T. Effects of two-week e-learning on eHealth literacy: a randomized controlled trial of Japanese Internet users[J]. PeerJ, 2018, 6: e5251. 96 周晓英, 宋丹, 张秀梅. 健康素养与健康信息传播利用的国家战略研究[J]. 图书与情报, 2015(4): 2-10. 97 Chapman E, Haby M M, Toma T S, et al. Knowledge translation strategies for dissemination with a focus on healthcare recipients: an overview of systematic reviews[J]. Implementation Science, 2020, 15(1): Article No.14. 98 Trethewey S P. Strategies to combat medical misinformation on social media[J]. Postgraduate Medical Journal, 2020, 96(1131): 4-6. 99 Krohn K M, Yu G, Lieber M, et al. The stanford global health media fellowship: training the next generation of physician communicators to fight health misinformation[J]. Academic Medicine, 2022, 97(7): 1004-1008. 100 Krohn K M, Crichlow R, McKinney Z J, et al. Introducing mass communications strategies to medical students: a novel short session for fourth-year students[J]. Academic Medicine, 2022, 97(7): 999-1003. 101 Armstrong P W, Naylor C D. Counteracting health misinformation: a role for medical journals?[J]. JAMA, 2019, 321(19): 1863-1864. 102 Adams R C, Challenger A, Bratton L, et al. Claims of causality in health news: a randomised trial[J]. BMC Medicine, 2019, 17(1): Article No.91. 103 Yang Q H, Luo Z F, Li M Y, et al. Understanding the landscape and propagation of COVID-19 misinformation and its correction on Sina Weibo[J]. Global Health Promotion, 2022, 29(1): 44-52. 104 Vraga E K, Bode L. Using expert sources to correct health misinformation in social media[J]. Science Communication, 2017, 39(5): 621-645. 105 Vraga E K, Bode L. Correcting what’s true: testing competing claims about health misinformation on social media[J/OL]. American Behavioral Scientist, (2022-08-25). https://doi.org/10.1177/00027642221118252. 106 Vraga E K, Bode L. I do not believe you: how providing a source corrects health misperceptions across social media platforms[J]. Information, Communication & Society, 2018, 21(10): 1337-1353. 107 Hermansyah A, Sukorini A I, Rahayu T P, et al. Exploring pharmacist experience and acceptance for debunking health misinformation in the social media[J]. Pharmacy Education, 2021, 21(2): 42-47. 108 Bautista J R, Zhang Y, Gwizdka J. Healthcare professionals’ acts of correcting health misinformation on social media[J]. International Journal of Medical Informatics, 2021, 148: 104375. 109 Walter N, Brooks J J, Saucier C J, et al. Evaluating the impact of attempts to correct health misinformation on social media: a meta-analysis[J]. Health Communication, 2021, 36(13): 1776-1784. 110 吴世文, 王一迪, 郑夏. 可信度的博弈: 伪健康信息与纠正性信息的信源及其叙事[J]. 全球传媒学刊, 2019, 6(3): 73-91. 111 Bautista J R, Zhang Y, Gwizdka J. Predicting healthcare professionals’ intention to correct health misinformation on social media[J]. Telematics and Informatics, 2022, 73: 101864. 112 Bode L, Vraga E K. See something, say something: correction of global health misinformation on social media[J]. Health Communication, 2018, 33(9): 1131-1140. 113 杨洸, 闻佳媛. 微信朋友圈的虚假健康信息纠错: 平台、策略与议题之影响研究[J]. 新闻与传播研究, 2020, 27(8): 26-43, 126. 114 MacFarlane D, Tay L Q, Hurlstone M J, et al. Refuting spurious COVID-19 treatment claims reduces demand and misinformation sharing[J]. Journal of Applied Research in Memory and Cognition, 2021, 10(2): 248-258. 115 Vraga E K, Bode L. Correction as a solution for health misinformation on social media[J]. American Journal of Public Health, 2020, 110(S3): S278-S280. 116 Dan V, Dixon G N. Fighting the infodemic on two fronts: reducing false beliefs without increasing polarization[J]. Science Communication, 2021, 43(5): 674-682. 117 Allington D, Duffy B, Wessely S, et al. Health-protective behaviour, social media usage and conspiracy belief during the COVID-19 public health emergency[J]. Psychological Medicine, 2021, 51(10): 1763-1769. 118 Syed-Abdul S, Fernandez-Luque L, Jian W S, et al. Misleading health-related information promoted through video-based social media: anorexia on YouTube[J]. Journal of Medical Internet Research, 2013, 15(2): e30. 119 Pant S, Deshmukh A, Murugiah K, et al. Assessing the credibility of the “YouTube approach” to health information on acute myocardial infarction[J]. Clinical Cardiology, 2012, 35(5): 281-285. |
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