Skip to main content
SpringerLink
Log in

Online Public Opinion Deduction Based on an Innovative Cellular Automata

  • Conference paper
  • First Online:
Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health (CyberDI 2019, CyberLife 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1137))

Abstract

As the Internet entered the 2.0 era, online users have become the main part of public opinion, and significant events happened around the world quickly disseminate through the Internet. Some negative comments usually are mixed into posts and articles. If let public opinion develop at will, some serious social events may trigger very fierce online volatility and they will have adverse effects on social stability. Therefore, to simulate the propagation mode of topic events on the Internet, and to predict the trending of public opinion on the entire Internet, this paper constructs a new type of model based on the idea of cellular automata and applies it to the online public opinion situation deduction. We arrange the nodes in new ways. An individual user is represented by a cell node in our model, and we build the cellular space based on directed graph to represent social network topology that users follow about each other in an innovative way. We delineate the users’ portrait based on the users’ attributes, design evolutionary rules based on the behaviors of users on the Internet and the law of information dissemination. Experiments with real data of online users showed that the proposed model in this paper had excellent performance, it not only reflected the law of information dissemination on the Internet, but also its deduction result was similar to the trending of real historical events.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Andrea, C., Luigi, C.: Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Med. Soc. 16(2), 340–358 (2012)

    Google Scholar 

  2. Chachra, A., Mehndiratta, P., Gupta, M.: Sentiment analysis of text using deep convolution neural networks. In: Tenth International Conference on Contemporary Computing (2017)

    Google Scholar 

  3. Chen, J., et al.: Research on agricultural monitoring system based on convolutional neural network. Future Gener. Comput. Syst. 88, 271–278 (2018)

    Article  Google Scholar 

  4. Codd, E.F., Ashenhurst, R.L.: Cellular automata (1968)

    MATH  Google Scholar 

  5. Deng, Q., Yi, L., Ma, Y., Hui, Z.: Information propagation and intervention on online social networks using cellular automata. Manag. Rev. 28(8), 106–114 (2016)

    Google Scholar 

  6. Fan, C., Wu, Y., Zhang, J., Zhao, T.: Research of public opinion hotspot detection model based on web big data. In: IEEE International Conference on Network Infrastructure & Digital Content (2017)

    Google Scholar 

  7. Forkan, A.R.M., Khalil, I., Atiquzzaman, M.: ViDiBiD: a learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data. Comput. Netw. 113, 244–257 (2017)

    Article  Google Scholar 

  8. Ha, D., Wu, Q.: Prediction for public opinion transmission based on epidemic model. J. Command Control 3(1), 57–60 (2017)

    Google Scholar 

  9. Hatton, T.: Public opinion on immigration in Europe: preference versus salience. Social Science Electronic Publishing (2017)

    Google Scholar 

  10. Huang, Y., Chen, F., You, D.: Research on the prediction of network public opinion based on hybrid algorithm. Inf. Sci. V36(2), 24–29 (2018)

    Google Scholar 

  11. Isa, D., Kallimani, V.P., Lee, L.H.: Using the self organizing map for clustering of text documents. Expert Syst. Appl. 36(5), 9584–9591 (2009)

    Article  Google Scholar 

  12. Chen, J., Wand, G., Xu, Y., Wang, H.: Study of the fitting and optimization for public opinion analysis indicator based on the multi-output neural network. Chin. High Technol. Lett. 29(1), 19–26 (2019)

    Google Scholar 

  13. Krueger, A.B., Jitka, M.: Attitudes and action: public opinion and the occurrence of international terrorism. Science 325(5947), 1534–1536 (2009)

    Article  Google Scholar 

  14. Lergetporer, P., Werner, K., Wößmann, L.: Public opinion on education policy in Germany. Social Science Electronic Publishing (2017)

    Google Scholar 

  15. Li, C., Kai, L., Wang, S., Management, S.O.: Propagation law and control decision of false public opinion in the government intervention based on multi-agent. J. Mod. Inf. 38(323), 55–61 (2018)

    Google Scholar 

  16. Lian, Z., Lan, Y., Xia, Y.: Research on multi-dimensional dynamical classification and prediction model of network public opinion with big data. Int. J. Drug Policy 37(5), 127–137+144 (2018)

    Google Scholar 

  17. Liu, Q., Jin, L.I., Xiao, R., Automation, S.O.: Trend prediction of public opinion propagation based on parameter inversion–an empirical study on Sina micro-blog. J. Comput. Appl. 5, 36 (2017)

    Google Scholar 

  18. Ma, K., Yu, Z., Ke, J., Bo, Y.: Stream-based live public opinion monitoring approach with adaptive probabilistic topic model. Soft Comput. 3, 1–20 (2018)

    Google Scholar 

  19. Maclennan, B., Kypri, K., Langley, J., Room, R.: Public sentiment towards alcohol and local government alcohol policies in New Zealand. Int. J. Drug Policy 23(1), 45–53 (2012)

    Article  Google Scholar 

  20. Mao, Q., Wang, C., Jin, H., Li, Y.: Research on internet public opinion cluster model of cellular automata based on fuzzy inference. J. Chin. Comput. Syst. 38(7), 1479–1484 (2017)

    Google Scholar 

  21. Qiu, X., Xiaohu, T., Liao, W.: Seir microblog public opinion commutication model with positive and negative feedbacks. Comput. Modernization 2018(2), 48–52 (2018)

    Google Scholar 

  22. Rapoport, A.: Theory of Self-Reproducing Automata (1966)

    Google Scholar 

  23. Rui-Qi, Y., Yue-Xia, Z.: Research on IC-SEIR public opinion propagation model based on complex network. Meas. Control Technol. 37(11), 78–83 (2018)

    Google Scholar 

  24. Shang, S., Shi, M., Shang, W., Hong, Z.: Research on public opinion based on big data. In: IEEE/ACIS International Conference on Computer & Information Science (2015)

    Google Scholar 

  25. Shi, R., Chen, F., Zhang, J.: Prediction of online public opinion based on combination grey model. J. Intell. V37(7), 105–110 (2018)

    Google Scholar 

  26. Sun, J.C., Zhou, R., Pei-Yue, L.I., Tian-Liang, L.U.: Research on the prediction of network public opinion based on recurrent neural network. Inf. Sci. 36(8), 118–122, 127 (2018)

    Google Scholar 

  27. Tian, Y., Yuan, W., Shao, L.: Online public opinion risk warning based on Bayesian network modeling. Lib. Inf. Serv. 26(2), 9.1–9.4 (2012)

    Google Scholar 

  28. Wang, C., Mao, Q., Xiang, T., Deng, C.: Mobile cellular automata of individualized network public opinion clustering model and simulation. Comput. Eng. Appl. 52(19), 122–127 (2016)

    Google Scholar 

  29. Xiao, B.: Hot topic real-time detection technology study and application. Dissertation, Beijing Univ. Post Telecommun. (2014)

    Google Scholar 

  30. Yixue, X., Ye, Y., Wencai, Z., Yuexin, L.: The research on abnormal data monitoring and application of network public opinion facing big data. J. Mod. Inf. 38(324), 82–87 (2018)

    Google Scholar 

  31. Yong-Yang, Y.U., Zhang, M.Z., Liu, C.Y., Bao-Hua, S.I.: A study on public opinion evolutionary model based on agent. Comput. Simul. 25(9), 9–12 (2008)

    Google Scholar 

  32. Li, Y.-Y.: Research on network public opinion spread based on decision theory cellular automata. Microelectron. Comput. 33(6), 29 (2016)

    Article  Google Scholar 

  33. You, D., Chen, F.: The literature review about the prediction of network public opinion in China. Inf. Sci. 12, 158–162 (2016)

    Google Scholar 

  34. You, D., Chen, F.: The literature review about the hotspot topic detection of network public opinion in China. J. Mod. Inf. 3, 167–173 (2017)

    Google Scholar 

  35. You, X., Liu, Q.: Research on microblogging information dissemination prediction based on infectious disease model. Comput. Appl. Softw. 33(5), 53–56 (2016)

    Google Scholar 

  36. Yu, W., Li, J.: Study on Wechat public opinion transmission mechanism basing on adaptive neuo fuzzy inference system and cellular automata. Inf. Res. 4, 7–10 (2016)

    Google Scholar 

  37. Yu, Y., Zhang, M., Liu, C., Luo, P., Cao, Y.: Factors analysis and modeling research of public opinion evolutionary model based on crisis events. In: National Simulator Academic Conference (2007)

    Google Scholar 

  38. Zeng, Z., Li, R.: A survey of research on dissemination model of network public opinion. J. CAEIT 2016(6), 24–29, 38 (2016)

    Google Scholar 

  39. Zeng, Z., Huang, C.: Research on public opinion heat trend prediction model of emergent infectious diseases based on BP neural network. J. Mod. Inf. 38(323), 39–46+54 (2018)

    Google Scholar 

  40. Zhang, H.P., Chen, Q.H.: Research on the prediction of network public opinion based on grey Markov model. Inf. Sci. 36(2), 75–79 (2018)

    Article  Google Scholar 

  41. Zhang, L., Chang, S., Jin, Y., Goh, M., Wu, Z.: Cross-network dissemination model of public opinion in coupled networks. Inf. Sci. 451–452, 240–252 (2018)

    Article  MathSciNet  Google Scholar 

  42. Zhe, F., Hao, Z., Qiang, C., Liu, S.: Application of naive Bayes classifier in stampede risk early-warning of large-scale activities. In: International Conference on Industrial Informatics-computing Technology (2017)

    Google Scholar 

Download references

Acknowledgement

This work is supported by the Key Research Program of Shandong Province (No. 2017GGX10140), National Natural Science Foundation of China (No. 61309024), the Fundamental Research Funds for the Central Universities (2015020031).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, China

    Xin Liu, Shuai Cao, Weishan Zhang, Xueli Wang & Liang Zheng

  2. Application on Improving Government Governance Capabilities National Engineering Laboratory, Guiyang, 550022, China

    Xin Liu, Shuai Cao, Yang Cao & Jie He

  3. CETC Big Data Research Institute Co., Ltd., Guiyang, 550022, China

    Yang Cao

  4. Information Science Academy of China Electronics Technology Group Corporation, Beijing, China

    Jie He

Authors
  1. Xin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuai Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weishan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xueli Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Liang Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Liu .

Editor information

Editors and Affiliations

  1. University of Science and Technology, Beijing, China

    Huansheng Ning

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, X. et al. (2019). Online Public Opinion Deduction Based on an Innovative Cellular Automata. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1137. Springer, Singapore. https://doi.org/10.1007/978-981-15-1922-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-1922-2_10

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1921-5

  • Online ISBN: 978-981-15-1922-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation