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Efficient CPS model based online opinion governance modeling and evaluation for emergency accidents

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Abstract

In the last decades, there have been much more public crisis accidents in the world such as H1N1, H7N9 and Ebola outbreak. It has been proved that our world has come into the time while public crisis accidents number was growing fast. Furthermore, crisis response to these public emergency accidents is always involved in a complex system consisting of cyber, physics and society domains (CPS Model). In order to collect and analyze these emergency accidents with higher efficiency, we need to design and adopt some new tools and models to analysis the online opinion. In this paper, we have proposed a new CPS Model based Online Opinion Governance system which constructed on cellphone APP for data collection including GIS information and online opinion and decision making in the back end. Our contributions include the graded risk classification method and accident classification method. Besides, we propose the group opinion polarization analysis method consisting two models and make promotion of the relative conditional entropy based context key word extraction method. Basing on these, we have built an efficient CPS Model based simulated emergency accident replying and handling system. It has been proved useful for emergency response in some real accidents in China such as Tianjin Explode accident and Haiyan Typhoon in recent years with detailed and vivid analysis result.

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References

  1. Joseph K, Landwehr PM, Carley KM (2014) An approach to selecting keywords to track on twitter during a disaster. In: Proceedings of the 11th International ISC. Conference – University Park. https://doi.org/10.1037/0003-066X.36.4.343RAM

  2. Slavkovikj V, Verstockt S, Hoecke SV et al. (2014) Review of wild fire detection using social media. Fire Safe J 68:109–118. https://doi.org/10.1016/j.firesaf.2014.05.021

    Article  Google Scholar 

  3. Deng Q, Liu Y, Zhang H et al. (2016) A new crowdsourcing model to assess disaster using microblog data in typhoon Haiyan[J]. Nat Hazards J Int Soc Prevent Mitigation Nat Hazards 84(2):1–16. https://doi.org/10.1007/s11069-016-2484-9

    Google Scholar 

  4. Ma YF, Deng Q, Wang XZ, Liu JQ, Zhang H (2014) Keyword-based semantic analysis of microblog for public opinion study in online collective behaviors, web-age information management lecture notes in computer science 2014, pp 44–55. https://doi.org/10.1007/978-3-319-11538-2_5

  5. German N, Leonie R, Astrid M et al. (2014) Psychosocial functions of social media usage in a disaster situation: a multi-methodological approach. Comput Hum Behav 34:28–38. https://doi.org/10.1016/j.chb.2014.01.021

    Article  Google Scholar 

  6. Onorati T, Malizia A, Diaz P, Aedo I (2014) Modeling an ontology on accessible evacuation routes for emergencies. Exp Syst Appl 41:7124–7134. https://doi.org/10.1016/j.eswa.2014.05.039

    Article  Google Scholar 

  7. Hadiguna RA, Kamil I, Delati S, Reed R (2014) Implementing a web-based decision support system for disaster logistics: a case study of an evacuation location assessment for Indonesia. Int J Disaster Risk Reduct 9:38–47. https://doi.org/10.1016/j.ijdrr.2014.02.004

    Article  Google Scholar 

  8. Campos V, Bandeira R, Bandeira A (2012) A method for evacuation route planning in disaster situations. Procedia - Soc Behav Sci 54:503–512. https://doi.org/10.1016/j.sbspro.2012.09.768

    Article  Google Scholar 

  9. Ndiaye IA, Neron E, Linot A, Monmarche N, Goerigk M (2014) A new model for macroscopic pedestrian evacuation planning with safety and duration criteria. Transport Res Procedia 2:486–494. https://doi.org/10.1016/j.trpro.2014.09.064

    Article  Google Scholar 

  10. Li R, Zhang H-p, Zhao Y-p, Shang J-y (2014) Automatic text summarization research based on topic model and information entropy[J]. Comput Sci 41(11a):298–332

    Google Scholar 

  11. Erdös P, Rényi A (1960) On the evolution of random graphs [J]. Publ Math Inst Hung Acad Sci Ser A 5:17–61. 05C80

    Google Scholar 

  12. Watts DJ, Strogatz SH (1998) Collective dynamics of “small world” networks [J]. Nature 393(4):440–442. https://doi.org/10.1038/30918

    Article  Google Scholar 

  13. Bababasi AL, Albertr (2512) Emergence of scaling in random networks [J]. Science 286(509):1999. https://doi.org/10.1126/science.286.5439.509

    Google Scholar 

  14. HaiBo H (2010) Research on the structure, evolution and dynamics of online social networks [D]. Shanghai Jiao Tong University, Doctoral dissertation

    Google Scholar 

  15. Li X, Chen G (2003) A local world evolving network model [J]. Phys A 323:274–286. https://doi.org/10.1016/S0378-4371(03)00604-6

    Article  Google Scholar 

  16. Macy MW, Kitts JA, Andreas F (2003) Polarization in dynamic networks: a Hopfield model of emergent structure [J]. The National Academies Press, (163)

  17. Maria MC, Neville AS, Ioannis M (2015) The concept of risk situation awareness provision: towards a new approach for assessing the DSA about the threats and vulnerabilities of complex socio-technical systems. Saf Sci 79:126–138. https://doi.org/10.1016/j.ssci.2015.05.012

    Article  Google Scholar 

  18. Newman MEJ, Clauset A (1863) Structure and inference in annotated networks. Nat Commun 7(1):2016. https://doi.org/10.1038/ncomms11863

    Google Scholar 

  19. Xiao Z, Travis M, Newman MEJ (2015) Identification of core-peripherystructure in networks. Phys Rev E 91:032803. https://doi.org/10.1103/PhysRevE.91.032803

    Article  Google Scholar 

  20. Wang L (2009) Amelioration of PageRank Algorithm [D]. Shanghai Jiao Tong University, Unpublished master’s thesis

    Google Scholar 

  21. Latané B (1981) The psychology of social impact [J]. Amer Psychol 36:343–365. https://doi.org/10.1037/0003-066X.36.4.343

    Article  Google Scholar 

  22. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities [J]. Proc Nat Acad Sci 79:2554–2558. https://doi.org/10.1073/pnas.79.8.2554

    Article  Google Scholar 

  23. Sun D, Zhang LH, Chen FX (2013) Comparative study on simulation performances of CORSIM and VISSIM for urban street network. Simul Model Pract Theory 37:18–29. https://doi.org/10.1016/j.simpat.2013.05.007

    Article  Google Scholar 

  24. Wilensky H (2014) Twitter as a navigator for stranded commuters during the great East Japan Earthquake. In: Proceedings of the 11th International ISCRAM conference. University Park

  25. Seppänen H, Virrantaus K (2015) Shared situational awareness and information quality in disaster management. Saf Sci 77:112–122. https://doi.org/10.1016/j.ssci.2015.03.018

    Article  Google Scholar 

  26. Mora K, Chang J, Beatson A, Morahan C (2015) Public perceptions of building seismic safety following the Canterbury earthquakes: a qualitative analysis using Twitter and focus groups. Int J Disaster Risk Reduct 13:1–9. https://doi.org/10.1016/j.ijdrr.2015.03.008

    Article  Google Scholar 

  27. Gaspar R, Pedroc C et al. (2016) Beyond positive or negative: qualitative sentiment analysis of social media reactions to unexpected stressful events. Comput Hum Behav 56:179–191. https://doi.org/10.1016/j.chb.2015.11.040

    Article  Google Scholar 

  28. Barabási A-L, Albert R (1999) Emergence of scaling in random networks [J]. Science 286:509–512

    Article  Google Scholar 

Download references

Acknowledgments

Thanks to Philosophy and Social Science Project of Education Ministry (No. 15JZD027), National Culture Support Foundation Project of China (2013BAH43F01), and National 973 Program Foundation Project of China (2013CB329600) in social network analysis. We appreciate direction from professor Hui Zhang andhis aid form Joint-Operated project from National Natural Science Foundation of China (NSFC) (Grants No. 91224008-14) from Tsinghua University.

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Correspondence to Xiao Long Deng or Yin Luan Yu.

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Deng, X.L., Yu, Y.L., Guo, D.H. et al. Efficient CPS model based online opinion governance modeling and evaluation for emergency accidents. Geoinformatica 22, 479–502 (2018). https://doi.org/10.1007/s10707-018-0319-4

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