错误信息网络传播的影响因素、传播机制及抑制策略
Online Misinformation: Dissemination Mechanism, Influence Factors and Suppression Strategies
DOI: 10.12677/AP.2021.111009, PDF,    国家社会科学基金支持
作者: 杨云霞, 陈功香：济南大学，教育与心理科学学院，山东 济南
关键词: 错误信息；传播机制；社交网络；内容特征；个体特征；Misinformation； Dissemination Mechanism； Social Network； Content Features； Individual Characteristics
摘要: 互联网时代，错误信息泛滥，严重威胁着社会秩序和公共安全，有效抑制错误信息的网络传播成为亟需解决的重大社会问题。文章首先介绍了错误信息网络传播的统计特征和传播机制；然后，从社交网络、内容特征和个体特征三个层面论述了影响错误信息传播的因素；最后，从心理学和计算机科学两个领域分别阐述了抑制错误信息传播的研究成果，并对存在的问题和未来研究趋势进行了讨论，以期有效应对网络错误信息。
Abstract: In the age of Internet, misinformation is rampant, which poses a serious threat to social order and public security, and effectively restraining the spread of misinformation has become a major social problem that needs to be solved. This paper first introduces the statistical properties and dissemination mechanism of online misinformation. Then, it discusses the factors that influence misinformation transmission from the aspects of social networks, content features and individual characteristics. In the end, the research achievements on the suppression of misinformation transmission are expounded from psychology and computer science, and the existing problems and future research trends are discussed in order to effectively deal with network misinformation.
文章引用：杨云霞, 陈功香 (2021). 错误信息网络传播的影响因素、传播机制及抑制策略. 心理学进展, 11(1), 67-75. https://doi.org/10.12677/AP.2021.111009

参考文献

[1] 彭晓哲, 崔芳, 焦璨, 李红(2018). 谣言传播的情境、内容、传播者与受众特征及相关认知神经科学研究. 心理科学, 41(4), 916-921.
[2] 温家林, 张增一(2018). 错误信息的产生、传播及识别和控制——错误信息已有研究评述. 科学与社会, 8(3), 108-122.
[3] Balmau, O., Guerraoui, R., Kermarrec, A. M., Maurer, A., Pavlovic, M., & Zwaenepoel, W. (2018). Limiting the Spread of Fake News on Social Media Platforms by Evaluating Users’ Trustworthiness. arXiv preprint arXiv:1808.09922
[4] Bessi, A. (2017). On the Statistical Properties of Viral Misinformation in Online Social Media. Physica A: Statistical Mechanics and Its Applications, 469, 459-470.[CrossRef] 
[5] Bursztyn, L., Rao, A., Roth, C., & Yanagizawa-Drott, D. (2020). Misinformation during a Pandemic. SSRN Electronic Journal, Working Paper No. 2020-44.[CrossRef] 
[6] Cook, J., Ecker, U., & Lewandowsky, S. (2015). Misinformation and How to Correct It. Emerging Trends in the Social and Behavioral Sciences. American Cancer Society.[CrossRef] 
[7] Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G. et al. (2016). The Spreading of Misinformation Online. Proceedings of the National Academy of Sciences of the United States of America, 113, 554-559.[CrossRef] [PubMed]
[8] Eckberg, D. A., Densley, J., & Dexter, K. (2018). When Legend Becomes Fact, Tweet the Legend: Information and Misinformation in the Age of Social Media. Journal of Behavioral and Social Sciences, 5, 148-156.
[9] Ecker, U. K. H., Lewandowsky, S., & Tang, D. T. W. (2010). Explicit Warnings Reduce but Do Not Eliminate the Continued Influence of Misinformation. Memory & Cognition, 38, 1087-1100.[CrossRef] 
[10] Ecker, U. K. H., Lewandowsky, S., Fenton, O., & Martin, K. (2014). Do People Keep Believing Because They Want to? Preexisting Attitudes and the Continued Influence of Misinformation. Memory & Cognition, 42, 292-304.[CrossRef] [PubMed]
[11] Guillory, J. J., & Geraci, L. (2013). Correcting Erroneous Infe-rences in Memory: The Role of Source Credibility. Journal of Applied Research in Memory and Cognition, 2, 201-209.[CrossRef] 
[12] Guillory, J. J., & Geraci, L. (2015). The Persistence of Erroneous Information in Memory: The Effect of Valence on the Acceptance of Corrected Information. Applied Cognitive Psychology, 30, 282-288.[CrossRef] 
[13] Guo, B., Ding, Y., Yao, L., Liang, Y., & Yu, Z. (2019). The Future of Misinformation Detection: New Perspectives and Trends. ACM Computing Surveys, 53, Article No. 68.
[14] Hameleers, M., & van der Meer, T. G. L. A. (2020). Misinformation and Polarization in a High-Choice Media Environment: How Effective Are Political Fact-Checkers? Communication Research, 47, 227-250.[CrossRef] 
[15] Henkel, L. A., & Mattson, M. E. (2011). Reading Is Believing: The Truth Effect and Source Credibility. Consciousness and Cognition, 20, 1705-1721.[CrossRef] [PubMed]
[16] Johnson, H. M., & Seifert, C. M. (1994). Sources of the Continued Influence Effect: When Misinformation Affects Later Inferences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20, 1420-1436.[CrossRef] 
[17] Johnson-Laird, P. N., Gawronski, B., & Strack, F. (2012). Mental Models and Consistency. In Cognitive Consistency: A Fundamental Principle in Social Cognition (pp. 225-243).
[18] Lewandowsky, S., Ecker, U. K. H., Seifert, C. M., Schwarz, N., & Cook, J. (2012). Misinformation and Its Correction: Continued Influence and Successful Debiasing. Psychological Science in the Public Interest, 13, 106-131.[CrossRef] [PubMed]
[19] Mian, A., & Khan, S. (2020). Coronavirus: The Spread of Misinformation. BMC Medicine, 18, Article No. 89.[CrossRef] [PubMed]
[20] O’Rear, A. E., & Radvansky, G. A. (2020). Failure to Accept Retractions: A Contribution to the Continued Influence Effect. Memory & Cognition, 48, 127-144.[CrossRef] [PubMed]
[21] Pennycook, G., & Rand, D. G. (2019). Fighting Misinformation on Social Media Using Crowdsourced Judgments of News Source Quality. Proceedings of the National Academy of Sciences of the United States of America, 116, 2521-2526.[CrossRef] [PubMed]
[22] Qiu, X., Diego, F. M. O., Sahami Shirazi, A., Flammini, A., & Menczer, F. (2017). Limited Individual Attention and Online Virality of Low-Quality Information. Nature Human Behaviour, 1, Article No. 132.[CrossRef] 
[23] Roozenbeek, J., Schneider, C. R., Dryhurst, S., Kerr, J. et al. (2020). Susceptibility to Misinformation about COVID-19 around the World. Royal Society Open Science, 7, Article ID: 201199.[CrossRef] [PubMed]
[24] Seifert, C. M. (2017). The Distributed Influence of Misinformation. Journal of Applied Research in Memory and Cognition, 6, 397-400.[CrossRef] 
[25] Shin, J., Jian, L., Driscoll, K., & Bar, F. (2016). Political Rumoring on Twitter during the 2012 US Presidential Election: Rumor Diffusion and Correction. New Media & Society, 19, 1214-1235.[CrossRef] 
[26] Shin, J., Jian, L., Driscoll, K., & Bar, F. (2018). The Diffusion of Misinformation on Social Media: Temporal Pattern, Message, and Source. Computers in Human Behavior, 83, 278-287.[CrossRef] 
[27] Swire, B., & Ecker, U. K. H. (2018). Misinformation and Its Correction: Cognitive Mechanisms and Recommendations for Mass Communication. Misinformation and Mass Audiences.
[28] Van Damme, I., & Smets, K. (2014). The Power of Emotion versus the Power of Suggestion: Memory for Emotional Events in the Misinformation Paradigm. Emotion, 14, 310-320.[CrossRef] [PubMed]
[29] Vosoughi, S., Roy, D., & Aral, S. (2018). The Spread of True and False News Online. Science, 359, 1146-1151.[CrossRef] [PubMed]
[30] Wagner, C., Mitter, S., Körner, C. & Strohmaier, M. (2012). When Social Bots Attack: Modeling Susceptibility of Users in Online Social Networks. Making Sense of Microposts, 2, 1951-1959.
[31] Wang, R., He, Y., Xu, J., & Zhang, H. (2020). Fake News or Bad News? Toward an Emotion-Driven Cognitive Dissonance Model of Misinformation Diffusion. Asian Journal of Communication, 30, 317-342.
[32] Wang, X., & Song, Y. (2020). Viral Misinformation and Echo Chambers: The Diffusion of Rumors about Genetically Modified Organisms on Social Media. Internet Research, 30, 1547-1564.[CrossRef] 
[33] Zhang, H., Zhang, H., Li, X., & Thai, M. T. (2015). Limiting the Spread of Misinformation While Effectively Raising Awareness in Social Networks. In M. Thai, N. Nguyen, & H. Shen (Eds.), Computational Social Networks. CSoNet 2015. Lecture Notes in Computer Science, Vol. 9197 (pp. 35-47). Cham: Springer.[CrossRef]