专题:突发公共卫生事件中网络谣言治理及个人信息保护研究

突发公共卫生事件下网络谣言传播逆转模型及仿真研究

  • 王晰巍 ,
  • 李玥琪 ,
  • 邱程程 ,
  • 胡欢
展开
  • 1 吉林大学管理学院 长春 130022;
    2 吉林大学大数据管理研究中心 长春 130022;
    3 吉林大学网络空间治理研究中心 长春 130022;
    4 江西理工大学经济管理学院 赣州 341000
王晰巍(ORCID:0000-0002-5850-0126),教授,博士生导师;邱程程(ORCID:0000-0002-0671-0340),硕士研究生;胡欢(ORCID:0000-0003-1285-2713),硕士研究生。

收稿日期: 2021-04-12

  修回日期: 2021-07-07

  网络出版日期: 2021-10-09

基金资助

本文系国家社会科学基金重大项目"大数据驱动的社交网络舆情主题图谱构建及调控策略研究"(项目编号:18ZDA310)和吉林大学国家发展与安全(生物安全)专项研究课题"生物安全风险治理的舆论传播体系研究(重点课题)"研究成果之一。

Reversal Model and Simulation of Online Rumor Propagation During Public Health Emergencies

  • Wang Xiwei ,
  • Li Yueqi ,
  • Qiu Chengcheng ,
  • Hu Huan
Expand
  • 1 School of Management, Jilin University, Changchun 130022;
    2 Research Center for Big Data Management, Jilin University, Changchun 130022;
    3 Research Center of Cyberspace Governance, Jilin University, Changchun 130022;
    4 School of Economics and Management, Jiangxi University of Science and Technology, Ganzhou 341000

Received date: 2021-04-12

  Revised date: 2021-07-07

  Online published: 2021-10-09

摘要

[目的/意义] 突发公共卫生事件期间,网民内生健康信息需求的同时缺乏健康信息的科学知识,为造谣者发布和传播网络谣言提供了机会。无效的科学知识及虚假新闻会对流行病爆发期间的社会稳定产生严重负面影响,扰乱社会秩序。因此构建有效的网络谣言传播-逆转模型以控制网络谣言的传播并降低其负面影响极为重要。[方法/过程] 研究采纳科学知识水平理论与网络谣言辟谣策略,基于SIR模型构建SCNDR网络谣言逆转系统动力学模型。采纳Anylogic软件对提出的SCNDR模型进行模拟仿真,并对模型的参数进行敏感性分析,提出了提升网络谣言传播-逆转模型逆转效率的具体策略。[结果/结论] 研究提出的SCNDR模型有效模拟了突发公共卫生事件期间网络谣言的传播-逆转过程,影响网络谣言逆转效率的关键因素分别为用户科学知识水平普及率、官方辟谣信息公开时间及轻信节点转化效率。

本文引用格式

王晰巍 , 李玥琪 , 邱程程 , 胡欢 . 突发公共卫生事件下网络谣言传播逆转模型及仿真研究[J]. 图书情报工作, 2021 , 65(19) : 4 -15 . DOI: 10.13266/j.issn.0252-3116.2021.19.001

Abstract

[Purpose/significance] During public health emergencies, due to folks' endogenous demand for health information and lack of scientific knowledge of health information, it stimulates the mass media to spread health information, and also provides opportunities for rumors to publish and spread online rumors. Ineffective scientific knowledge and false news will have a serious negative impact on social stability and disrupt social order during the outbreak of epidemics. Therefore, it is very important to build an effective reversal model of online rumor propagation to control the spread of online rumors and reduce its negative impact.[Method/process] The research adopted the theory of scientific knowledge level and the strategy of online rumors reputation, and constructed the SCNDR online rumor reversal model based on SIR model. The Anylogic software was adopted to simulate the SCNDR model, and the sensitivity analysis of the parameters of the model was carried out.[Result/conclusion] The SCNDR model proposed in this study effectively simulates the reversal process of online rumors during public health emergencies. The key factors affecting the reversal efficiency of online rumors are the penetration rate of users' scientific knowledge level, the public time of official information and the conversion efficiency of credulous users.

参考文献

[1] 世界卫生组织(WHO). 关于2019新型冠状病毒疫情的《国际卫生条例(2005)》突发事件委员会第二次会议的声明[EB/OL].[2020-02-25]. https://www.who.int/zh/news/item/30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov).
[2] LIU P L, HUANG L V. 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.
[3] ISLAM A, LAATO S, TALUKDER S, et al. Misinformation sharing and social media fatigue during covid-19:an affordance and cognitive load perspective[J]. Technological forecasting and social change, 2020, 159:120201.
[4] ZHAO L, CUI H, QIU X, et al. SIR rumor spreading model in the new media age[J]. Physica A:statistical mechanics and its applications, 2013, 392(4):995-1003.
[5] ZHAO L, WANG J, CHEN Y, et al. SIHR rumor spreading model in social networks[J]. Physica A:statistical mechanics & its applications, 2012, 391(7):2444-2453.
[6] WANG J, ZHAO L, HUANG R. SIRaRu rumor spreading model in complex networks[J]. Physica A:statistical mechanics and its applications, 2014, 398(15):43-55.
[7] JIANG G, LI S, LI M. Dynamic rumor spreading of public opinion reversal on weibo based on a two-stage SPNR model[J]. Physica A:statistical mechanics and its applications, 2020, 558[2021-07-01]. https://doi.org/10.1016/j.physa.2020.125005.
[8] KERMACK W O, MCKENDRICK A G. A contribution to the mathematical theory of epidemics[J]. Bulletin of mathematical biology, 1991, 53(1/2):57-87.
[9] CHENG Y, HUO L, ZHAO L. Dynamical behaviors and control measures of rumor-spreading model in consideration of the infected media and time delay[J]. Information sciences, 2021, 564(3):237-253.
[10] HUO L, CHEN S, ZHAO L. Dynamic analysis of the rumor propagation model with consideration of the wise man and social reinforcement[J]. Physica A:statistical mechanics and its applications, 2021.571[2021-07-01]. https://doi.org/10.1016/j.physa.2021.125828
[11] 杨湘浩,阚顺玉,叶旭,等.基于超网络的突发事件网络谣言传播模型研究[J/OL].情报理论与实践:1-12[2021-07-01].http://kns.cnki.net/kcms/detail/11.1762.G3.20210518.1855.006.html.
[12] 吴大伟,胡小飞,艾文华.突发公共卫生事件高低热度谣言传播组态路径研究——基于模糊集定性比较分析[J/OL].情报科学:1-8[2021-07-01].http://kns.cnki.net/kcms/detail/22.1264.g2.20200911.1533.028.html.
[13] 杨晗迅,周德群,马静,等.基于不确定性损失函数和任务层级注意力机制的多任务谣言检测研究[J/OL].数据分析与知识发现:1-14[2021-07-01].http://kns.cnki.net/kcms/detail/10.1478.G2.20210409.1230.002.html.
[14] 王晰巍,张柳,黄博,等.基于区块链的网络谣言甄别模型及仿真研究[J].情报学报,2021,40(2):194-203.
[15] 崔金栋,陈思远,李晨雨.基于大数据的多类型网络谣言类型平息方式实证研究——以"新冠肺炎疫情期间谣言"为例[J].情报理论与实践,2021,44(4):67-73.
[16] GIORNO, VIRGINIA, SPINA, et al. Rumor spreading models with random denials[J]. Physica A:Statistical mechanics and its applications, 2016, 461:569-576.
[17] ALKHODAIR S A, DING S, FUNG B. Detecting breaking news rumors of emerging topics in social media[J]. Information processing & management, 2019.57(2)[2021-07-01]. https://doi.org/10.1016/j.ipm.2019.02.016.
[18] MARTIN M K, VAN S, DAVID S. The second information revolution:digitalization brings opportunities and concerns for public health[J]. The European journal of public health, 2019(Supplement_3):3-6.
[19] KATZ E, SHIBUTANI T. Improvised news:a sociological study of rumor[J]. American sociological review, 1969, 34(5):781.
[20] DUNN H B, ALLEN C A. Rumors, urban legends and internet hoaxes[C]//Proceedings of the annual meeting of the Association of Collegiate Marketing Educators. 2005:85-91.
[21] MANSELL R, LIVINGSTONE S, BECKETT. Tackling the information crisis:a policy framework for media system resilience[J]. London:London School of Economics and Political Science, 2018.
[22] RADCLIFFE R. The mathematical theory of infectious diseases and its applications[J]. Applied statistics, 1977, 26(1):85.
[23] COHEN J E. Infectious diseases of humans:dynamics and control[J]. Jama the journal of the American Medical Association, 1992, 268(23):3381.
[24] PASTOR-SATORRAS R, VESPIGNANI A. Epidemic spreading in scale-free networks[J]. Physical review letters, 2000, 86(14):3200-3203.
[25] ZANETT H D. Dynamics of rumor propagation on small-world networks[J]. Physical review E, 2002, 65(4):041908.
[26] HUO L,CHEN S. Rumor propagation model with consideration of scientific knowledge level and social reinforcement in heterogeneous network-ScienceDirect[J]. Physica A:statistical mechanics and its applications, 2020, 559[2021-07-01]. https://doi.org/10.1016/j.physa.2020.125063.
[27] AFASSINOU, KOMI. Analysis of the impact of education rate on the rumor spreading mechanism[J]. Physica A:statistical mechanics & its applications, 2014, 414:43-52.
[28] HUO L A, HUANG P Q. Study of the impact of science popularization and media coverage on the transmission of unconfirmed information[J]. Systems engineering-theory & practice, 2014, 34(2):365-375.
[29] HU Y, PAN Q, HOU W, et al. Rumor spreading model considering the proportion of wisemen in the crowd[J]. Physica A:statistical mechanics and its applications, 2018, 505:1084-1094.
[30] PROC NATL ACAD SCI USA. Assessment of individual radionuclide distributions from the Fukushima nuclear accident covering central-east Japan[J]. Radiation exposure effects on humans food chain environment, 2011, 108(49):19526-19529.
[31] CAO B,HAN S H,ZHEN J. Modeling of knowledge transmission by considering the level of forgetfulness in complex networks[J]. Physica A:statistical mechanics and its applications, 2016, 451:277-287.
[32] HE H, YC C, YM D. Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading[J]. Applied mathematics and computation, 2021, 388:125536.
[33] PAL A, CHUA A, GOH H L. How do users respond to online rumor rebuttals?[J]. Computers in human behavior, 2019, 106:106243.
[34] WEEKS B E, KELLY G R. Electoral consequences of political rumors:motivated reasoning, candidate rumors, and vote choice during the 2008 U.S. presidential election[J]. International journal of public opinion research, 2014(4):401-422.
[35] Y LIAN, Y LIU, DONG X. Strategies for controlling false online information during natural disasters:the case of typhoon mangkhut in China[J/OL]. Technology in society,2020, 62, 101265[2021-07-01]. https://doi.org/10.1016/j.techsoc.2020.101265.
[36] JIE W, WANG W, WANG C. How the anti-rumor kills the rumor:Conflicting information propagation in networks[C]//2016 IEEE international conference on communications. IEEE, 2016:1-6.
[37] TRIPATHY R M, BAGCHI A, MEHTA S. A study of rumor control strategies on social networks[C]//Proceedings of the 19th ACM international conference on information and knowledge management. New York:ACM, 2010:1817-1820.
[38] JI K, LIU J, XIANG G. Anti-rumor dynamics and emergence of the timing threshold on complex network[J]. Physica A:statistical mechanics & its applications, 2014, 411:87-94.
[39] YANG J, LEE S. Framing the MERS information crisis:An analysis on online news media's rumour coverage[J]. Journal of contingencies and crisis management, 2020(4):386-398.
[40] ZHAO Z J, LIU Y M, WANG K X. An analysis of rumor propagation based on propagation force[J]. Physica A:statistical mechanics & its applications, 2016, 443:263-271.
[41] XIA, LING-LING, SONG, et al. Rumor spreading model considering hesitating mechanism in complex social networks[J]. Physica A:statistical mechanics & its applications, 2015,437:295-303.
[42] NEKOVEE M, MORENO Y, BIANCONI G, et al. Theory of rumour spreading in complex social networks[J]. Physica A:statistical mechanics & its applications, 2008, 374(1):457-470.
[43] 百度指数. "双黄连口服液"百度搜索指数[EB/OL].[2021-03-30]. http://index.baidu.com/v2/index.html#/.
[44] JIANG M, GAO Q, ZHUANG J. Reciprocal spreading and debunking processes of online misinformation:a new rumor spreading-debunking model with a case study[J]. Physica A:statistical mechanics and its applications, 2021, 565:125572.
[45] HUNT K, WANG B, ZHUANG J. Misinformation debunking and cross-platform information sharing through Twitter during Hurricanes Harvey and Irma:a case study on shelters and ID checks[J]. Natural hazards, 2020(3):861-883.
[46] VOSOUGHI S, ROY D, ARAL S. The spread of true and false news online[J]. Science, 2018, 359(6380):1146-1151.
[47] APUKE O D, OMAR B. User motivation in fake news sharing during the COVID-19 pandemic:an application of the uses and gratification theory[J]. Online information review, 2020.45(1):220-239[2021-07-01]. https://doi.org/10.1108/OIR-03-2020-0116.
[48] SURI V R, MAJID S, CHANG Y K, et al. Assessing the influence of health literacy on health information behaviors:a multi-domain skills-based approach[J]. Patient education & counseling, 2016, 99(6):1038-1045.
[49] FU H, D DONG, D FENG, et al. To share or not to share:a cross-sectional study on health information sharing and its determinants among chinese rural chronic patients[J]. Journal of health communication, 2017, 22(10), 800-807.
[50] DON N. Health literacy as a public health goal:a challenge for contemporary health education and communication strategies into the 21st century[J]. Health promotion international, 2000(3):259-267.
[51] LEDFORD C, CAFFERTY L A, RUSSELL T C. The influence of health literacy and patient activation on patient information seeking and sharing[J]. Journal of health communication, 2015, 20(S2):77-82.
[52] PAL A, CHUA A, GOH H L. Debunking rumors on social media:the use of denials[J]. Computers in human behavior, 2019, 96(7):110-122.
[53] TIAN Y, DING X. Rumor spreading model with considering debunking behavior in emergencies[J]. Applied mathematics and computation, 2019, 363[2021-07-01]. https://doi.org/10.1016/j.amc.2019.124599.
[54] PAL A, CHUA A, GOH H L, et al. Does KFC sell rat? Analysis of tweets in the wake of a rumor outbreak[J]. Aslib journal of information management, 2017, 69(6):660-673.
[55] LZ A, FAN Y A, GUI G B, et al. Modeling the dynamics of rumor diffusion over complex networks[J]. Informationsciences, 2021, 562, 240-258.
[56] WANG B, ZHUANG J. Rumor response, debunking response, and decision makings of misinformed Twitter users during disasters[J]. Natural hazards, 2018, 93(3):1145-1162.
[57] CHEN S, JIANG H, LI L, et al. Dynamical behaviors and optimal control of rumor propagation model with saturation incidence on heterogeneous networks[J]. Chaos solitons & fractals, 2020, 140:110206.
[58] ZENG J, CHAN C, FU K. How social media construct "truth" around crisis events:weibo's rumor management strategies after the 2015 tianjin blasts[J]. Policy & Internet, 2017, 9(3):297-320.
[59] WOOD M J. Propagating and debunking conspiracy theories on twitter during the 2015-2016 zika virus outbreak[J]. Cyberpsychology behavior & social networking, 2018, 21(8):485-490.
[60] PAL A, CHUA A, GOH H L. Debunking rumors on social media:the use of denials[J]. Computers in human behavior, 2019, 96(7):110-122.
文章导航

/