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doi: 10.3934/jimo.2022037
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## Uncertain comprehensive evaluation of the spreading intensity of internet rumors in the new media era

 1 Business School, Shandong Normal University, Jinan, 250014, China 2 School of Information Engineering, Shandong Youth University of Political Science, Key Laboratory of Intelligent Information Processing, Technology and Security in Universities of Shandong, Jinan, 250103, China 3 Business School, Shandong Normal University, Jinan, 250014, China

*Corresponding author: Fengming Liu

Received  October 2021 Revised  December 2021 Early access March 2022

Fund Project: This research was supported in part by the National Social Science Foundation of China (No. 14BTQ049, 21BGL001), the National Natural Science Foundation of China (71701115), Shandong Natural Science Foundation (ZR2020MG003), Special Project for Internet Development of Social Science Planning Special Program of Shandong Province (17CHLJ23)

Rumor is a kind of abnormal information, which spreads faster than normal information, and has high transmission intensity. The existing rumor spreading formulas give an explanation of rumor spreading intensity. However, there has been a lack of corresponding research on how to measure. This paper presents a method. Firstly, the rumor spreading formula is combed and commented, and the important influencing factors in the process of rumor spreading are extracted. Secondly, the influencing factors of rumor information dissemination are analyzed, and an uncertain evaluation index list of network rumor intensity is constructed, and the dissemination intensity of rumor information is measured based on uncertain comprehensive evaluation. Finally, the effectiveness of the application of the evaluation model is verified by a numerical example, and operational policy suggestions are given according to the analysis of the results.

Citation: Chunhua Gao, Yufu Ning, Fengming Liu, Meiling Jin. Uncertain comprehensive evaluation of the spreading intensity of internet rumors in the new media era. Journal of Industrial and Management Optimization, doi: 10.3934/jimo.2022037
##### References:
 [1] Y. Mocquard, B. Sericola and E. Anceaume, Probabilistic analysis of rumor-spreading time, Informs Journal on Computing, 32 (2020), 172-181.  doi: 10.1287/ijoc.2018.0845. [2] A. N. Zehmakan and S. Galam, Rumor spreading: a trigger for proliferation or fading away, Chaos, 30 (2020), 73-122.  doi: 10.1063/5.0006984. [3] J. L. Zhang and F. F. Duan, Research on the formation, evolution and governance of Internet rumors in the post truth era, Western Radio and Television, 42 (2021), 96–98 + 107. [4] Z. F. Zhang and Y. Wang, Reasons, harm and countermeasures of rumors in the era of social media – Based on the rumor spread of the new crown pneumonia epidemic, News Forum, 34 (2020), 62-65. [5] L. Tao, On the governance mechanism innovation of network rumors of major emergencies – Based on the comparative analysis of Internet rumors in SARS and COVID-19, Future Communication, 28 (2021), 59-68. [6] R. Guo and W. M. Wang, Characteristics, causes, harm and countermeasures of image rumors in new media – Taking 60 image rumors as research samples, Young Journalists, 03 (2020), 31-32. [7] J. Y. Ren, Y. G. Wang and Y Chai, Group spread rumor model of rational interaction on social networks, Computer Technology and Development, 31 (2021), 105-112. [8] X. H. Yang, S. Y. Kan, X. Ye, et al., Research on network rumor propagation model of Emergencies Based on hypernetwork, Information Theory and Practice, (2021), 1–12. [9] G. H. Qiu, X. Y. Li and K. X. Han, Simulation of information interception model of network rumor propagation power in social networks, Computer Simulation, 38 (2021), 209–212 + 217. [10] S. Hosseini and M. A. Azgomi, A model for malware propagation in scale-free networks based on rumor spreading process, Computer Networks, 108 (2016), 97-107.  doi: 10.1016/j.comnet.2016.08.010. [11] Y. L. Zan, DSIR double-rumors spreading model in complex networks, Chaos Solitons & Fractals, 110 (2018), 191-202.  doi: 10.1016/j.chaos.2018.03.021. [12] A. Wang, Research on node importance evaluation method based on complex network structure characteristics, Master thesis, People's Public Security University of China, 2020. [13] L Zhao, X Qiu and X Wang, Rumor spreading model considering forgetting and remembering mechanisms in inhomogeneous networks, Physica A Statistical Mechanics & Its Applications, 392 (2013), 987-994.  doi: 10.1016/j.physa.2012.10.031. [14] J. R. Li, H. J. Jiang and X. H. Mei, Dynamical analysis of rumor spreading model in multi-lingual environment and heterogeneous complex networks, Information Sciences, 536 (2020), 391-408.  doi: 10.1016/j.ins.2020.05.037. [15] G. G. Fang, Research on social psychology in rumors – A grounded theoretical analysis based on 244 rumors, New Media and Society, 01 (2020), 1-16. [16] X. J. Ding and S. Q. Liu, Research on online social network rumor propagation behavior based on evolutionary game, Operations Research and Management, 29 (2020), 11-21. [17] J. Gu, W. Li and X. Cai, The effect of the forget-remember mechanism on spreading, European Physical Journal B, 62 (2008), 247-255.  doi: 10.1140/epjb/e2008-00139-4. [18] S. Xu, Study on influencing factors of microblog rumor spread, Master thesis, Tianjin Normal University, 2014. [19] C. Hou, Research on the propagation path and effect of microblog rumors, Master thesis, Lanzhou University, 2014. [20] L. L. Xia, Research on the propagation formula and governance countermeasures of wechat rumors, Master thesis, Huazhong University of Science and Technology, 2017. [21] F. T. Feng, Preliminary study on rumor propagation in wechat circle of friends, Master thesis, Party School of the CPC Central Committee, 2019. [22] Z. Q. Jiang, Reflections on crisis education in colleges and universities based on rumor formula under the background of major epidemic situation, Journal of Liupanshui Normal University, 32 (2020), 17-20. [23] Y. Zhong, Governance strategy of Internet rumors in the era of we media, Economic Research Guide, 08 (2018), 198-199. [24] B. Liu, Uncertainty Theory, 2$^{nd}$ edition, Springer-Verlag, Berlin, 2004. doi: 10.1007/978-3-540-39987-2. [25] X. Gao, Some properties of continuous uncertain measure, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 17 (2009), 419-426.  doi: 10.1142/S0218488509005954. [26] W. Liu and J. P. Xu, Some Properties on Expected Value Operator for Uncertain Variables, Information- An International Interdisciplinary Journal, 13 (2010), 1693-1699. [27] Y. Sheng and S. Kar, Some results of moments of uncertain variable through inverse uncertainty distribution, Fuzzy Optimization and Decision Making, 14 (2015), 57-76.  doi: 10.1007/s10700-014-9193-1. [28] C. S. Chen, J. Y. Tang and J. B. Xiao, Uncertainty distribution of some composite uncertain variables, International of Uncertain Fuzziness and Knowledge-Based Syetems, 25 (2017), 545-555.  doi: 10.1142/S0218488517500234. [29] R. Gao, H. Ahmadzade and M. Esfahani, Covariance and Pseudo-Covariance of Complex Uncertain Variables, Journal of Intelligent & Fuzzy Systems, 36 (2019), 241-251.  doi: 10.3233/JIFS-181233. [30] Y.C. Li, R. Peng, I. Kucukkoc and et al., System reliability optimization for an assembly line under uncertain random environment, Computers & Industrial Engineering, 146 (2020). doi: 10.1016/j.cie.2020.106540. [31] P. Daniele, Heterogeneity-controlled uncertain optimization of pump-and-treat systems explained through geological entropy, GEM - International Journal on Geomathematics, 11 (2020), 234-243. [32] Y. C. Li, Y. Fu and X. W. Tang, Optimizing the reliability and efficiency for an assembly line that considers uncertain task time attributes, IEEE Access, 7 (2019), 34121-34130.  doi: 10.1109/ACCESS.2019.2897730. [33] C. Wang, Y. Ni and X. Yang, The inventory replenishment policy in an uncertain production-inventory-routing system, Journal of Industrial and Management Optimization, (2021). doi: 10.3934/jimo.2021196. [34] X. Y. Li, L. Gao and W. W. Wang, Particle swarm optimization hybridized with genetic algorithm for uncertain integrated process planning and scheduling with interval processing time, Computers & Industrial Engineering, 135 (2019), 1036-1046.  doi: 10.1016/j.cie.2019.04.028. [35] W. C. Lio and B. D. Liu, Shortage index and shortage time of uncertain production risk process, IEEE Transactions on Fuzzy Systems, 28 (2020), 2856-2863.  doi: 10.1109/TFUZZ.2019.2945246. [36] Z. Y, Hu ang, C. L. Zhu and J. W. Gao, Stability analysis for uncertain differential equation by Lyapunov's second method, Fuzzy Optimization and Decision Making, 20 (2020), 129-144.  doi: 10.1007/s10700-020-09336-7. [37] Y. H. Sheng and G. Shi, Stability in mean of multi-dimensional uncertain differential equation, Applied Mathematics and Computation, 353 (2019), 178-188.  doi: 10.1016/j.amc.2019.02.008. [38] Z. Y. Zhu, Z. S. Zhao and J. Zhang, Adaptive fuzzy control design for synchronization of chaotic time-delay system, Information Sciences, 535 (2020), 225-241.  doi: 10.1016/j.ins.2020.05.056. [39] M. Q. Yin, W. Y. Qian and W. Li, Portfolio selection models based on cross-entropy of uncertain variables, Intelligent & Fuzzy Systems, 31 (2016), 737-747.  doi: 10.3233/JIFS-169006. [40] H. Ahmadzade, R. Gao and M. H. Dehghan, Partial triangular entropy of uncertain random variables and its application, Ambient Intelligence and Humanized Computing, 9 (2018), 1455-1464.  doi: 10.1007/s12652-017-0565-6. [41] L. Chen, R. Gao and Y. X. Bian, Elliptic entropy of uncertain random variables with application to portfolio selection, Soft Computing, 25 (2021), 1925-1939.  doi: 10.1007/s00500-020-05266-z. [42] A. Sajedi and G. Yari, Order $v$ entropy and cross entropy of uncertain variables for portfolio selection, International Journal of Fuzzy Logic and Intelligent Systems, 20 (2020), 35-42.  doi: 10.5391/IJFIS.2020.20.1.35. [43] J. Liu, J. S. Xie and H. Ahmadzade, A new measure of indeterminacy for uncertain variables with application to portfolio selection, Journal of Intelligent and Fuzzy Systems, 40 (2021), 1-5.  doi: 10.3233/JIFS-202073. [44] Y.D. Shu and Y.G. Zhu, Optimistic value based optimal control for uncertain linear singular systems and application to a dynamic input-output model, ISA Transactions, 71 (2017), 235-251.  doi: 10.1016/j.isatra.2017.08.007. [45] Y.G. Zhu, Uncertain optimal control with application to a portfolio selection model, Cybernetics and Systems, 41 (2010), 535-547.  doi: 10.1080/01969722.2010.511552. [46] X. T. Ge and Y.G. Zhu, A necessary condition of optimality for uncertain optimal control problem, Fuzzy Optimization and Decision Making, 12 (2013), 41-51.  doi: 10.1007/s10700-012-9147-4. [47] Y. F. Ning and T. Y. Su, A multilevel approach for modelling vehicle routing problem with uncertain travelling time, Journal of Intelligent Manufacturing, 28 (2017), 683-688.  doi: 10.1007/s10845-014-0979-3. [48] X. F Yang and Y. D. Ni, Extreme values problem of uncertain heat equation, Journal of Industrial and Management Optimization, 15 (2019), 1995-2008.  doi: 10.3934/jimo.2018133. [49] L. F. Jia, W. C. Lio and and W. Chen, Extreme values, first hitting time and time integral of solution of uncertain spring vibration equation, Journal of Intelligent & Fuzzy Systems, 38 (2020), 3201-3211.  doi: 10.3233/JIFS-191179. [50] M. Jin, Y. Ning and B. Li, Uncertain KOL selection with multiple constraints in advertising promotion, IEEE Access, 9 (2021), 142839-142878.  doi: 10.1109/ACCESS.2021.3121518. [51] D. McGregor, The major determinants of the prediction of social events, Journal of Abnormal and Social Psychology, 33 (1938), 179-204.  doi: 10.1037/h0062931. [52] G.W. Allport and L. J. Postman, The Psychology of Rumor, Holt, Rinehart & Winston: New York, NY, USA, (1947). [53] A. Chorus, The basic law of rumor, Journal of Abnormal and Social Psychology, 48 (1953), 313-314.  doi: 10.1037/h0060600. [54] R.L. Rosnow, Inside Rumor: A Personal Journey, Am. Psychol, 46 (1991), 484-496.  doi: 10.1037/0003-066X.46.5.484. [55] Q. Wang and F. Yu, Improvement and verification of the rumor propagation formula of Allport and Postman: analysis of Sina Weibo rumor based on the casualties caused by the Northeast Tiger, International Press, 39 (2017), 47-67. [56] J. Wu and C. Ma, Rumor propagation formula: traceability, correction and development, Press, 13 (2015), 20-23. [57] Y. Hu, Mass communication effect: problems and countermeasures, Xinhua Publishing House, 2000. [58] B. J. Hu, Crisis communication management: schools, paradigms and paths, China Renmin University Press, 2009. [59] L. T. Zhang, Revision and enlightenment of rumor propagation formula – Also on "gossip" and "rumor", News Research Introduction, 7 (2016), 54–55 + 67.

show all references

##### References:
 [1] Y. Mocquard, B. Sericola and E. Anceaume, Probabilistic analysis of rumor-spreading time, Informs Journal on Computing, 32 (2020), 172-181.  doi: 10.1287/ijoc.2018.0845. [2] A. N. Zehmakan and S. Galam, Rumor spreading: a trigger for proliferation or fading away, Chaos, 30 (2020), 73-122.  doi: 10.1063/5.0006984. [3] J. L. Zhang and F. F. Duan, Research on the formation, evolution and governance of Internet rumors in the post truth era, Western Radio and Television, 42 (2021), 96–98 + 107. [4] Z. F. Zhang and Y. Wang, Reasons, harm and countermeasures of rumors in the era of social media – Based on the rumor spread of the new crown pneumonia epidemic, News Forum, 34 (2020), 62-65. [5] L. Tao, On the governance mechanism innovation of network rumors of major emergencies – Based on the comparative analysis of Internet rumors in SARS and COVID-19, Future Communication, 28 (2021), 59-68. [6] R. Guo and W. M. Wang, Characteristics, causes, harm and countermeasures of image rumors in new media – Taking 60 image rumors as research samples, Young Journalists, 03 (2020), 31-32. [7] J. Y. Ren, Y. G. Wang and Y Chai, Group spread rumor model of rational interaction on social networks, Computer Technology and Development, 31 (2021), 105-112. [8] X. H. Yang, S. Y. Kan, X. Ye, et al., Research on network rumor propagation model of Emergencies Based on hypernetwork, Information Theory and Practice, (2021), 1–12. [9] G. H. Qiu, X. Y. Li and K. X. Han, Simulation of information interception model of network rumor propagation power in social networks, Computer Simulation, 38 (2021), 209–212 + 217. [10] S. Hosseini and M. A. Azgomi, A model for malware propagation in scale-free networks based on rumor spreading process, Computer Networks, 108 (2016), 97-107.  doi: 10.1016/j.comnet.2016.08.010. [11] Y. L. Zan, DSIR double-rumors spreading model in complex networks, Chaos Solitons & Fractals, 110 (2018), 191-202.  doi: 10.1016/j.chaos.2018.03.021. [12] A. Wang, Research on node importance evaluation method based on complex network structure characteristics, Master thesis, People's Public Security University of China, 2020. [13] L Zhao, X Qiu and X Wang, Rumor spreading model considering forgetting and remembering mechanisms in inhomogeneous networks, Physica A Statistical Mechanics & Its Applications, 392 (2013), 987-994.  doi: 10.1016/j.physa.2012.10.031. [14] J. R. Li, H. J. Jiang and X. H. Mei, Dynamical analysis of rumor spreading model in multi-lingual environment and heterogeneous complex networks, Information Sciences, 536 (2020), 391-408.  doi: 10.1016/j.ins.2020.05.037. [15] G. G. Fang, Research on social psychology in rumors – A grounded theoretical analysis based on 244 rumors, New Media and Society, 01 (2020), 1-16. [16] X. J. Ding and S. Q. Liu, Research on online social network rumor propagation behavior based on evolutionary game, Operations Research and Management, 29 (2020), 11-21. [17] J. Gu, W. Li and X. Cai, The effect of the forget-remember mechanism on spreading, European Physical Journal B, 62 (2008), 247-255.  doi: 10.1140/epjb/e2008-00139-4. [18] S. Xu, Study on influencing factors of microblog rumor spread, Master thesis, Tianjin Normal University, 2014. [19] C. Hou, Research on the propagation path and effect of microblog rumors, Master thesis, Lanzhou University, 2014. [20] L. L. Xia, Research on the propagation formula and governance countermeasures of wechat rumors, Master thesis, Huazhong University of Science and Technology, 2017. [21] F. T. Feng, Preliminary study on rumor propagation in wechat circle of friends, Master thesis, Party School of the CPC Central Committee, 2019. [22] Z. Q. Jiang, Reflections on crisis education in colleges and universities based on rumor formula under the background of major epidemic situation, Journal of Liupanshui Normal University, 32 (2020), 17-20. [23] Y. Zhong, Governance strategy of Internet rumors in the era of we media, Economic Research Guide, 08 (2018), 198-199. [24] B. Liu, Uncertainty Theory, 2$^{nd}$ edition, Springer-Verlag, Berlin, 2004. doi: 10.1007/978-3-540-39987-2. [25] X. Gao, Some properties of continuous uncertain measure, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 17 (2009), 419-426.  doi: 10.1142/S0218488509005954. [26] W. Liu and J. P. Xu, Some Properties on Expected Value Operator for Uncertain Variables, Information- An International Interdisciplinary Journal, 13 (2010), 1693-1699. [27] Y. Sheng and S. Kar, Some results of moments of uncertain variable through inverse uncertainty distribution, Fuzzy Optimization and Decision Making, 14 (2015), 57-76.  doi: 10.1007/s10700-014-9193-1. [28] C. S. Chen, J. Y. Tang and J. B. Xiao, Uncertainty distribution of some composite uncertain variables, International of Uncertain Fuzziness and Knowledge-Based Syetems, 25 (2017), 545-555.  doi: 10.1142/S0218488517500234. [29] R. Gao, H. Ahmadzade and M. Esfahani, Covariance and Pseudo-Covariance of Complex Uncertain Variables, Journal of Intelligent & Fuzzy Systems, 36 (2019), 241-251.  doi: 10.3233/JIFS-181233. [30] Y.C. Li, R. Peng, I. Kucukkoc and et al., System reliability optimization for an assembly line under uncertain random environment, Computers & Industrial Engineering, 146 (2020). doi: 10.1016/j.cie.2020.106540. [31] P. Daniele, Heterogeneity-controlled uncertain optimization of pump-and-treat systems explained through geological entropy, GEM - International Journal on Geomathematics, 11 (2020), 234-243. [32] Y. C. Li, Y. Fu and X. W. Tang, Optimizing the reliability and efficiency for an assembly line that considers uncertain task time attributes, IEEE Access, 7 (2019), 34121-34130.  doi: 10.1109/ACCESS.2019.2897730. [33] C. Wang, Y. Ni and X. Yang, The inventory replenishment policy in an uncertain production-inventory-routing system, Journal of Industrial and Management Optimization, (2021). doi: 10.3934/jimo.2021196. [34] X. Y. Li, L. Gao and W. W. Wang, Particle swarm optimization hybridized with genetic algorithm for uncertain integrated process planning and scheduling with interval processing time, Computers & Industrial Engineering, 135 (2019), 1036-1046.  doi: 10.1016/j.cie.2019.04.028. [35] W. C. Lio and B. D. Liu, Shortage index and shortage time of uncertain production risk process, IEEE Transactions on Fuzzy Systems, 28 (2020), 2856-2863.  doi: 10.1109/TFUZZ.2019.2945246. [36] Z. Y, Hu ang, C. L. Zhu and J. W. Gao, Stability analysis for uncertain differential equation by Lyapunov's second method, Fuzzy Optimization and Decision Making, 20 (2020), 129-144.  doi: 10.1007/s10700-020-09336-7. [37] Y. H. Sheng and G. Shi, Stability in mean of multi-dimensional uncertain differential equation, Applied Mathematics and Computation, 353 (2019), 178-188.  doi: 10.1016/j.amc.2019.02.008. [38] Z. Y. Zhu, Z. S. Zhao and J. Zhang, Adaptive fuzzy control design for synchronization of chaotic time-delay system, Information Sciences, 535 (2020), 225-241.  doi: 10.1016/j.ins.2020.05.056. [39] M. Q. Yin, W. Y. Qian and W. Li, Portfolio selection models based on cross-entropy of uncertain variables, Intelligent & Fuzzy Systems, 31 (2016), 737-747.  doi: 10.3233/JIFS-169006. [40] H. Ahmadzade, R. Gao and M. H. Dehghan, Partial triangular entropy of uncertain random variables and its application, Ambient Intelligence and Humanized Computing, 9 (2018), 1455-1464.  doi: 10.1007/s12652-017-0565-6. [41] L. Chen, R. Gao and Y. X. Bian, Elliptic entropy of uncertain random variables with application to portfolio selection, Soft Computing, 25 (2021), 1925-1939.  doi: 10.1007/s00500-020-05266-z. [42] A. Sajedi and G. Yari, Order $v$ entropy and cross entropy of uncertain variables for portfolio selection, International Journal of Fuzzy Logic and Intelligent Systems, 20 (2020), 35-42.  doi: 10.5391/IJFIS.2020.20.1.35. [43] J. Liu, J. S. Xie and H. Ahmadzade, A new measure of indeterminacy for uncertain variables with application to portfolio selection, Journal of Intelligent and Fuzzy Systems, 40 (2021), 1-5.  doi: 10.3233/JIFS-202073. [44] Y.D. Shu and Y.G. Zhu, Optimistic value based optimal control for uncertain linear singular systems and application to a dynamic input-output model, ISA Transactions, 71 (2017), 235-251.  doi: 10.1016/j.isatra.2017.08.007. [45] Y.G. Zhu, Uncertain optimal control with application to a portfolio selection model, Cybernetics and Systems, 41 (2010), 535-547.  doi: 10.1080/01969722.2010.511552. [46] X. T. Ge and Y.G. Zhu, A necessary condition of optimality for uncertain optimal control problem, Fuzzy Optimization and Decision Making, 12 (2013), 41-51.  doi: 10.1007/s10700-012-9147-4. [47] Y. F. Ning and T. Y. Su, A multilevel approach for modelling vehicle routing problem with uncertain travelling time, Journal of Intelligent Manufacturing, 28 (2017), 683-688.  doi: 10.1007/s10845-014-0979-3. [48] X. F Yang and Y. D. Ni, Extreme values problem of uncertain heat equation, Journal of Industrial and Management Optimization, 15 (2019), 1995-2008.  doi: 10.3934/jimo.2018133. [49] L. F. Jia, W. C. Lio and and W. Chen, Extreme values, first hitting time and time integral of solution of uncertain spring vibration equation, Journal of Intelligent & Fuzzy Systems, 38 (2020), 3201-3211.  doi: 10.3233/JIFS-191179. [50] M. Jin, Y. Ning and B. Li, Uncertain KOL selection with multiple constraints in advertising promotion, IEEE Access, 9 (2021), 142839-142878.  doi: 10.1109/ACCESS.2021.3121518. [51] D. McGregor, The major determinants of the prediction of social events, Journal of Abnormal and Social Psychology, 33 (1938), 179-204.  doi: 10.1037/h0062931. [52] G.W. Allport and L. J. Postman, The Psychology of Rumor, Holt, Rinehart & Winston: New York, NY, USA, (1947). [53] A. Chorus, The basic law of rumor, Journal of Abnormal and Social Psychology, 48 (1953), 313-314.  doi: 10.1037/h0060600. [54] R.L. Rosnow, Inside Rumor: A Personal Journey, Am. Psychol, 46 (1991), 484-496.  doi: 10.1037/0003-066X.46.5.484. [55] Q. Wang and F. Yu, Improvement and verification of the rumor propagation formula of Allport and Postman: analysis of Sina Weibo rumor based on the casualties caused by the Northeast Tiger, International Press, 39 (2017), 47-67. [56] J. Wu and C. Ma, Rumor propagation formula: traceability, correction and development, Press, 13 (2015), 20-23. [57] Y. Hu, Mass communication effect: problems and countermeasures, Xinhua Publishing House, 2000. [58] B. J. Hu, Crisis communication management: schools, paradigms and paths, China Renmin University Press, 2009. [59] L. T. Zhang, Revision and enlightenment of rumor propagation formula – Also on "gossip" and "rumor", News Research Introduction, 7 (2016), 54–55 + 67.
Evaluation index of network rumor intensity
 Primary Index Secondary Index Event-Level Ambiguity Importance Abnormality News Value Stimulate Personal-Level Critical Anxiety Involvement Transmitter Influence Medium Literacy Attention Governmental-Level Transparency Control Media-Level Media Responsibility Social-Level Credibility Asymmetry Correlation Number of Times Social Mood Density Hierarchy
 Primary Index Secondary Index Event-Level Ambiguity Importance Abnormality News Value Stimulate Personal-Level Critical Anxiety Involvement Transmitter Influence Medium Literacy Attention Governmental-Level Transparency Control Media-Level Media Responsibility Social-Level Credibility Asymmetry Correlation Number of Times Social Mood Density Hierarchy
Weight statistics of primary evaluation indicators
 Primary Evaluation Indicators The Most Important Important Ordinary Less Important Unimportant Event-Level $(A)$ 11 17 10 2 0 Personal-Level $(B)$ 13 22 2 3 0 Governmental-Level $(C)$ 25 12 3 0 0 Media-Level $(D)$ 19 15 4 2 0 Social-Level $(E)$ 10 23 5 1 1
 Primary Evaluation Indicators The Most Important Important Ordinary Less Important Unimportant Event-Level $(A)$ 11 17 10 2 0 Personal-Level $(B)$ 13 22 2 3 0 Governmental-Level $(C)$ 25 12 3 0 0 Media-Level $(D)$ 19 15 4 2 0 Social-Level $(E)$ 10 23 5 1 1
Weight statistics of secondary evaluation indicators
 Secondary Evaluation Indicators The Most Important Important Ordinary Less Important Unimportant Ambiguity $(A_{11})$ 16 16 3 3 2 Importance $(A_{12})$ 18 17 2 2 1 Abnormality $(A_{13})$ 14 12 10 3 1 News Value $(A_{14})$ 14 9 3 10 4 Stimulate $(A_{15})$ 8 18 9 4 1 Critical $(A_{21})$ 18 14 6 2 0 Anxiety $(A_{22})$ 10 11 15 2 2 Involvement $(A_{23})$ 13 12 13 2 0 Transmitter Influence $(A_{24})$ 19 15 5 1 0 Medium Literacy $(A_{25})$ 14 17 8 1 0 Attention $(A_{26})$ 16 18 4 1 1 Transparency $(A_{31})$ 24 11 4 1 0 Control $(A_{32})$ 22 11 5 2 0 Media $(A_{41})$ 9 19 8 4 0 Responsibility $(A_{42})$ 22 15 2 1 0 Credibility $(A_{51})$ 22 10 5 2 1 Asymmetry $(A_{52})$ 18 18 3 0 1 Correlation $(A_{53})$ 11 16 10 3 0 Number of Times $(A_{54})$ 13 11 13 3 0 Social Mood $(A_{55})$ 9 22 6 3 0 Density Hierarchy $(A_{56})$ 8 16 8 7 1
 Secondary Evaluation Indicators The Most Important Important Ordinary Less Important Unimportant Ambiguity $(A_{11})$ 16 16 3 3 2 Importance $(A_{12})$ 18 17 2 2 1 Abnormality $(A_{13})$ 14 12 10 3 1 News Value $(A_{14})$ 14 9 3 10 4 Stimulate $(A_{15})$ 8 18 9 4 1 Critical $(A_{21})$ 18 14 6 2 0 Anxiety $(A_{22})$ 10 11 15 2 2 Involvement $(A_{23})$ 13 12 13 2 0 Transmitter Influence $(A_{24})$ 19 15 5 1 0 Medium Literacy $(A_{25})$ 14 17 8 1 0 Attention $(A_{26})$ 16 18 4 1 1 Transparency $(A_{31})$ 24 11 4 1 0 Control $(A_{32})$ 22 11 5 2 0 Media $(A_{41})$ 9 19 8 4 0 Responsibility $(A_{42})$ 22 15 2 1 0 Credibility $(A_{51})$ 22 10 5 2 1 Asymmetry $(A_{52})$ 18 18 3 0 1 Correlation $(A_{53})$ 11 16 10 3 0 Number of Times $(A_{54})$ 13 11 13 3 0 Social Mood $(A_{55})$ 9 22 6 3 0 Density Hierarchy $(A_{56})$ 8 16 8 7 1
Statistical table of enhancement evaluation
 Primary Evaluation Indicators Secondary Evaluation Indicators Strongest Stronger Strong Less Strong Not Strong $A$ $A_{11}, A_{12}$, $A_{13}, A_{14}, A_{15}$ 9, 14, 9, 7, 9 16, 18, 17, 16, 20 12, 7, 10, 10, 8 3, 1, 3, 7, 3 0, 0, 1, 0, 0 $B$ $B_{21}, B_{22}, B_{23}$, $B_{24}, B_{25}, B_{26}$ 7, 10, 13, 17, 14, 6 18, 19, 18, 18, 19, 23 11, 9, 5, 5, 5, 10 4, 2, 3, 0, 2, 1 0, 0, 1, 0, 0, 0 $C$ $C_{31}, C_{32}$ 18, 18 13, 14 7, 6 0, 1 2, 1 $D$ $D_{41}, D_{42}$ 10, 14 18, 17 8, 6 3, 2 1, 1 $E$ $E_{51}, E_{52}, E_{53}$, $E_{54}, E_{55}, E_{56}$ 12, 11, 11, 11, 10, 11 19, 18, 17, 14, 21, 20 4, 9, 9, 12, 8, 7 3, 0, 2, 3, 1, 2 2, 2, 1, 0, 0, 0
 Primary Evaluation Indicators Secondary Evaluation Indicators Strongest Stronger Strong Less Strong Not Strong $A$ $A_{11}, A_{12}$, $A_{13}, A_{14}, A_{15}$ 9, 14, 9, 7, 9 16, 18, 17, 16, 20 12, 7, 10, 10, 8 3, 1, 3, 7, 3 0, 0, 1, 0, 0 $B$ $B_{21}, B_{22}, B_{23}$, $B_{24}, B_{25}, B_{26}$ 7, 10, 13, 17, 14, 6 18, 19, 18, 18, 19, 23 11, 9, 5, 5, 5, 10 4, 2, 3, 0, 2, 1 0, 0, 1, 0, 0, 0 $C$ $C_{31}, C_{32}$ 18, 18 13, 14 7, 6 0, 1 2, 1 $D$ $D_{41}, D_{42}$ 10, 14 18, 17 8, 6 3, 2 1, 1 $E$ $E_{51}, E_{52}, E_{53}$, $E_{54}, E_{55}, E_{56}$ 12, 11, 11, 11, 10, 11 19, 18, 17, 14, 21, 20 4, 9, 9, 12, 8, 7 3, 0, 2, 3, 1, 2 2, 2, 1, 0, 0, 0
Importance ranking of secondary intensity evaluation indexes
 Primary Evaluation Indicators Secondary Evaluation Indicators Event-Level Importance $>$ Ambiguity $>$ Abnormality $>$ Stimulate $>$ News Value Personal-Level Transmitter Influence $>$ Critical $>$ Attention $>$ Medium Literacy $>$ Involvement $>$ Anxiety Governmental-Level Transparency $>$ Control Media-Level Responsibility $>$ Media Social-Level Asymmetry $>$ Credibility $>$ Social Mood $>$ Correlation $>$ Number of Times $>$ Density Hierarchy
 Primary Evaluation Indicators Secondary Evaluation Indicators Event-Level Importance $>$ Ambiguity $>$ Abnormality $>$ Stimulate $>$ News Value Personal-Level Transmitter Influence $>$ Critical $>$ Attention $>$ Medium Literacy $>$ Involvement $>$ Anxiety Governmental-Level Transparency $>$ Control Media-Level Responsibility $>$ Media Social-Level Asymmetry $>$ Credibility $>$ Social Mood $>$ Correlation $>$ Number of Times $>$ Density Hierarchy
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