Advertisement

GeoInformatica

, Volume 22, Issue 2, pp 311–334 | Cite as

The national geographic characteristics of online public opinion propagation in China based on WeChat network

  • Chuan Ai
  • Bin Chen
  • Lingnan He
  • Kaisheng Lai
  • Xiaogang Qiu
Article
First Online:
Received:
Revised:
Accepted:

Abstract

Offline networks have been the subject of intense academic scrutiny for many decades, but we still know little about the nationwide spatial interaction patterns and its application for public opinion management of online social networks. With the aim of uncovering the geographic interaction characteristics of online public opinion propagation, we analyze a large dataset obtained from WeChat, the most popular social media application in China, and construct the spatial interaction network G, which contains 359 city-nodes. It is found that the communities in the network and the administrative division corresponded well with each other, and cities with high betweenness and degree also develop well in the economy. Public opinion propagation depends on the state of online interaction. The findings indicate that public opinion should be managed separately by regions divided according to the community division, and different regions should adopt different management methods according to their economic, historical and political characteristics. In our work, the possibility and opportunity is presented to study the spatial interaction patterns of online public opinion propagation with the massive behavioral data and the methods of complex network.

Keywords

Online interaction network Public opinion online Community detection Regional characteristics 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This study is supported by National Key Research & Development (R&D) Plan under Grant No. 2017YFC0803300 and the National Natural Science Foundation of China under Grant Nos. 71673292,61503402 and Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion. We also thank Fibonacci Consulting Co. Ltd. for the big dataset

References

  1. 1.
    Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B (2007) Measurement and analysis of online social networks. Paper presented at the Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, San DiegoGoogle Scholar
  2. 2.
    Kumar R, Novak J, Tomkins A (2010) Structure and Evolution of Online Social Networks. In: Yu PS, Han J, Faloutsos C (eds) Link Mining: Models, Algorithms, and Applications. Springer New York, New York, pp 337–357.  https://doi.org/10.1007/978-1-4419-6515-8_13 CrossRefGoogle Scholar
  3. 3.
    Barrat A, Barthélemy M, Pastor-Satorras R, Vespignani A (2004) The architecture of complex weighted networks. Proc Natl Acad Sci U S A 101(11):3747–3752.  https://doi.org/10.1073/pnas.0400087101 CrossRefGoogle Scholar
  4. 4.
    Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393(6684):440–442CrossRefGoogle Scholar
  5. 5.
    Barabási A-L, Albert R (1999) Emergence of Scaling in Random Networks. Science 286(5439):509CrossRefGoogle Scholar
  6. 6.
    Benevenuto F, Rodrigues T, Cha M, Almeida V (2009) Characterizing user behavior in online social networks. Paper presented at the Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference, ChicagoGoogle Scholar
  7. 7.
    Schneider F, Feldmann A, Krishnamurthy B, Willinger W (2009) Understanding online social network usage from a network perspective. Paper presented at the Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference, ChicagoGoogle Scholar
  8. 8.
    Nazir A, Raza S, Chuah C-N (2008) Unveiling facebook: a measurement study of social network based applications. Paper presented at the Proceedings of the 8th ACM SIGCOMM conference on Internet measurement, Vouliagmeni, GreeceGoogle Scholar
  9. 9.
    Granovetter M (1978) Threshold Models of Collective Behavior. Am J Sociol 83(6):1420–1443.  https://doi.org/10.1086/226707 CrossRefGoogle Scholar
  10. 10.
    Watts DJ (2002) A simple model of global cascades on random networks. Proc Natl Acad Sci U S A 99(9):5766–5771.  https://doi.org/10.1073/pnas.082090499 CrossRefGoogle Scholar
  11. 11.
    Gong NZ, Xu W, Huang L, Mittal P, Stefanov E, Sekar V, Song D (2012) Evolution of social-attribute networks: measurements, modeling, and implications using google+. Paper presented at the Proceedings of the 2012 ACM conference on Internet measurement conference, BostonGoogle Scholar
  12. 12.
    Gautier K, Francesco C, Carlo R, Vincent DB (2009) Urban gravity: a model for inter-city telecommunication flows. Journal of Statistical Mechanics: Theory and Experiment 2009(07):L07003Google Scholar
  13. 13.
    Roth C, Kang SM, Batty M, Barthélemy M (2011) Structure of urban movements: polycentric activity and entangled hierarchical flows. PLoS One 6(1):e15923.  https://doi.org/10.1371/journal.pone.0015923 CrossRefGoogle Scholar
  14. 14.
    Liu Z, Huang Y (2014) Community detection from location-tagged networks[C]// ACM Sigspatial International Conference on Advances in Geographic Information Systems. ACM, pp 525–528Google Scholar
  15. 15.
    Ratti C, Sobolevsky S, Calabrese F, Andris C, Reades J, Martino M, Claxton R, Strogatz SH (2010) Redrawing the map of Great Britain from a network of human interactions. PLoS One 5(12):e14248.  https://doi.org/10.1371/journal.pone.0014248 CrossRefGoogle Scholar
  16. 16.
    Moyano LG, Thomae ORM, Frias-Martinez E (2012) Uncovering the Spatio-temporal Structure of Social Networks Using Cell Phone Records. Paper presented at the Proceedings of the 2012 I.E. 12th International Conference on Data Mining WorkshopsGoogle Scholar
  17. 17.
    Ullman EL, Boyce RR (1980) Geography as spatial interaction. University of Washington Press 44:13–27Google Scholar
  18. 18.
    Noronha VT, Goodchild MF (2005) Modeling Interregional Interaction: Implications for Defining Functional Regions. Ann Assoc Am Geogr 82(1):86–102CrossRefGoogle Scholar
  19. 19.
    Castells M (1996) Rise of the Network Society: The Information Age: Economy, Society and Culture. Blackwell Publishers, Inc. 48(2):132–134Google Scholar
  20. 20.
    Guanghua C, Yu L, Li S, Yong G (2017) Understanding the effects of administrative boundary in sampling spatially embedded networks. Physica A: Statistical Mechanics and its Applications 466:616–625.  https://doi.org/10.1016/j.physa.2016.09.023 CrossRefGoogle Scholar
  21. 21.
    Blondel V, Krings G, Thomas I (2010) Regions and borders of mobile telephony in belgium and in the brussels metropolitan zone. Brussels Studies 42(4):1–12Google Scholar
  22. 22.
    Freeman LC (1977) A Set of Measures of Centrality Based on Betweenness. Sociometry 40(1):35–41.  https://doi.org/10.2307/3033543 CrossRefGoogle Scholar
  23. 23.
    Guimerà R, Mossa S, Turtschi A, Amaral LAN (2005) The Worldwide Air Transportation Network: Anomalous Centrality, Community Structure, and Cities' Global Roles. Proc Natl Acad Sci U S A 102(22):7794–7799CrossRefGoogle Scholar
  24. 24.
    Onnela J, Saramäki J, Hyvönen J, Szabó G, de Menezes M, Kaski K, Barabási A, Kertész J (2007) Analysis of a large-scale weighted network of one-to-one human communication. New J Phys 9(6).  https://doi.org/10.1088/1367-2630/9/6/179
  25. 25.
    Guanghua C, Jean-Claude T, Daoqin T, Li S, Yu L (2016) Uncovering regional characteristics from mobile phone data: A network science approach. Pap Reg Sci 95(3):613–631.  https://doi.org/10.1111/pirs.12149 CrossRefGoogle Scholar
  26. 26.
    Palla G, Barabási A-L, Vicsek T (2007) Quantifying social group evolution. Nature 446(7136):664–667CrossRefGoogle Scholar
  27. 27.
    Lambiotte R, Blondel V, de Kerchove C, Huens E, Prieur C, Smoreda Z, Van Dooren P (2008) Geographical dispersal of mobile communication networks. Physica A: Statistical Mechanics and its Applications 387.  https://doi.org/10.1016/j.physa.2008.05.014
  28. 28.
    Song G, Yu L, Yaoli W, Xiujun M (2013) Discovering Spatial Interaction Communities from Mobile Phone Data. Trans GIS 17(3):463–481.  https://doi.org/10.1111/tgis.12042 CrossRefGoogle Scholar
  29. 29.
    Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E Stat Nonlinear Soft Matter Phys 69(2 Pt 2):026113.  https://doi.org/10.1103/physreve.69.026113 CrossRefGoogle Scholar
  30. 30.
    Sobolevsky S, Szell M, Campari R, Couronné T, Smoreda Z, Ratti C (2013) Delineating geographical regions with networks of human interactions in an extensive set of countries. PLoS One 8(12):e81707.  https://doi.org/10.1371/journal.pone.0081707 CrossRefGoogle Scholar
  31. 31.
    Montis AD, Barthelemy M, Chessa A, Vespignani A (2005) The structure of Inter-Urban traffic: A weighted network analysis. Physics 34(5):905–924Google Scholar
  32. 32.
    Fortunato S (2010) Community detection in graphs. Phys Rep 486(3–5):75–174.  https://doi.org/10.1016/j.physrep.2009.11.002 CrossRefGoogle Scholar
  33. 33.
    Lancichinetti A, Fortunato S (2009) Community detection algorithms: a comparative analysis. Phys Rev E Stat Nonlinear Soft Matter Phys 80(5 Pt 2):056117.  https://doi.org/10.1103/physreve.80.056117 CrossRefGoogle Scholar
  34. 34.
    Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci U S A 105(4):1118–1123.  https://doi.org/10.1073/pnas.0706851105 CrossRefGoogle Scholar
  35. 35.
    Gautreau A, Barrat A, Barthélemy M (2009) Microdynamics in stationary complex networks. Proc Natl Acad Sci U S A 106(22):8847–8852.  https://doi.org/10.1073/pnas.0811113106 CrossRefGoogle Scholar
  36. 36.
    Holme P (2003) Network dynamics of ongoing social relationships. Physics.  https://doi.org/10.1209/epl/i2003-00505-4
  37. 37.
    Krings G, Karsai M, Bernhardsson S, Blondel VD, Saramäki J (2012) Effects of time window size and placement on the structure of an aggregated communication network. EPJ Data Science 1(1):4.  https://doi.org/10.1140/epjds4 CrossRefGoogle Scholar
  38. 38.
    Jian S, Ke X, Meina S, Xiaosu Z (2014) Credibility evaluation method of domestic IP address database. Journal of Computer Applications (z2):4-6,15Google Scholar
  39. 39.
    Freeman RL (2005) Fundamentals of Telecommunications. Wiley-IEEE Press.  https://doi.org/10.1002/0471224162.ch1
  40. 40.
    Hubert L, Arabie P (1985) Comparing partitions. J Classif 2(1):193–218.  https://doi.org/10.1007/BF01908075 CrossRefGoogle Scholar
  41. 41.
    State B, Park P, Weber I, Macy M (2015) The mesh of civilizations in the global network of digital communication. PLoS One 10(5):e0122543.  https://doi.org/10.1371/journal.pone.0122543 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Chuan Ai
    • 1
  • Bin Chen
    • 1
  • Lingnan He
    • 2
  • Kaisheng Lai
    • 2
  • Xiaogang Qiu
    • 1
  1. 1.College of Information System and ManagementNational University of Defense TechnologyChangshaChina
  2. 2.School of Communication and DesignSun yat-sen UniversityGuangzhouChina

Personalised recommendations

Cite article

Buy options