Abstract
Rumor spreading has become an increasingly prominent issue as the development of online social networks. Previous rumor spreading studies tend to adopt the classical epidemic models. This paper from another perspective, proposes a Logistic regression model to predict spreading probability of a rumor. Firstly, some critical factors that influence rumors spreading are identified, including counter-rumor mechanism, local socio-economic development level, information development level index, and environmental factors. After that, this paper fits a multiple logistic regression model based on three years’ water pollution rumors spreading data in China, and assesses its goodness of fit. Some influential rumors in the data are analyzed. Then, this paper tests the prediction power of the proposed model. Finally, according to the significance level of the parameters in the model, as well as the tentative prediction results, this paper gives some managerial advice to prevent rumor spreading in some major cities in China.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.
References
Zan, Y., Wu, J., Li, P., Yu, Q.: Sicr rumor spreading model in complex networks: counterattack and selfresistance. Phys. Stat. Mech. Appl. 405, 159170 (2014)
Zhao, L., Cui, H., et al.: Sir rumor spreading model in the new media age. Phys. Stat. Mech. Appl. 392(4), 9951003 (2013)
Zhang, Z., Zhang, Z.: An interplay model for rumour spreading and emergency development. Phys. Stat. Mech. Appl. 388(19), 41594166 (2012)
Li, D., Ma, J., Tian, Z., Zhu, H.: An evolutionary game for the diffusion of rumor in complex networks. Phys. Stat. Mech. Appl. 433, 5158 (2015)
Zhen, Q., Tang, S., Xiao, Z., Zheng, Z.: The independent spreaders involved sir rumor model in complex networks. Phys. Stat. Mech. Appl. 429, 95–102 (2015)
Daley, D.J., Kendall, D.G.: Epidemics and rumours. Nature 204(4963), 1118–1118 (1964)
Misako, T., Kazuya, S., et al.: Rumor diffusion and convergence during the 3.11 earthquake: a Twitter case study. Plos One 10(4), 0121443 (2015)
Xia, L., Jiang, G., Song, Y., Song, B.: Modeling and analyzing the interaction between network rumors and authoritative information. Entropy 17(1), 471–482 (2015)
Roberto, J., Piqueira, C.: Rumor propagation model: an equilibrium study. Math. Probl. Eng. 4, 242–256 (2010)
Tian, R., Zhang, X., Liu, Y.: Ssic model: a multilayer model for intervention of online rumors spreading. Phys. Stat. Mech. Appl. 427, 181–191 (2015)
Tan, Z., Ning, J., et al.: Ecrmodel: an elastic collision-based rumor-propagation model in online social networks. IEEE Access 4(99), 6105–6120 (2017)
Wan, C., Li, T., Sun, Z.: (2017) Global stability of a seir rumor spreading model with demographics on scale-free networks. Adv. Differ. Equ. 1, 253 (2017)
He, Z., Cai, Z., Yu, J., et al.: (2017) Cost-efficient strategies for restraining rumor spreading in mobile social networks. IEEE Trans. Veh. Technol. 99, 1–1 (2017)
Nagao, M., Suto, K., Ohuchi, A.: (2010) A media information analysis for implementing effective countermeasure against harmful rumor. J. Phys. Conf. Ser. 221, 012004 (2010)
Zubiaga, A., Ahmet, A., Bontcheva, K., Liakata, M., Procter, R.: Detection and resolution of rumours in social media: a survey. ACM Comput. Surv. 51(2), 32 (2018)
Aricat, R.: Effacing the dilemma of the rumouring subject: a value-oriented approach towards studying misinformation on social media. J. Hum. Values 24, 097168581773357 (2017)
Wang, X., et al.: Rumor propagation model with considering refutation mechanism in inhomogeneous networks. Syst. Eng. 2015(12) (2015)
Zhu, X., Liu, F.: Research on behavior model of rumor maker based on system dynamics. Complexity 06, 1–9 (2017)
Kim, S., Kim, S.: Impact of the Fukushima nuclear accident on belief in rumors: the role of risk perception and communication. Sustainability 9(11), 2188 (2017)
Yang, L., Xu, S., Tourassi, S.: Detecting rumors through modeling information propagation networks in a social media environment. IEEE Trans. Comput. Soc. Syst. 3(2), 46–62 (2017)
Agresti, A.: An introduction to categorical data analysis (2007). Wiley-Interscience
Thompson, B.: Stepwise regression and stepwise discriminant analysis need not apply here: a guidelines editorial. Educ. Psychol. Meas. 55(4), 525–534 (1995)
Sas Publishing.: SAS/STAT 9.2 User’s Guide: The MIXED Procedure—Book Excerpt (2009)
Cody, R.: SAS Functions by Example (2013)
Acknowledgments
We are thankful for financial support from the National Natural Science Foundations (Grant No. 71601134), and China Postdoctoral Science Foundation (Grant No. 2017M612983).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, S., Li, Z. (2020). Prediction of Rumors Wide-Spreading on Social Media by Logistic Regression Modeling: Taking Water Resource Pollution Rumors Spreading as an Example. In: Xu, J., Ahmed, S., Cooke, F., Duca, G. (eds) Proceedings of the Thirteenth International Conference on Management Science and Engineering Management. ICMSEM 2019. Advances in Intelligent Systems and Computing, vol 1001. Springer, Cham. https://doi.org/10.1007/978-3-030-21248-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-21248-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21247-6
Online ISBN: 978-3-030-21248-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)