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Breaking news dissemination in the media via propagation behavior based on complex network theory

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  • Liu, Nairong
  • An, Haizhong
  • Gao, Xiangyun
  • Li, Huajiao
  • Hao, Xiaoqing

Abstract

The diffusion of breaking news largely relies on propagation behaviors in the media. The tremendous and intricate propagation relationships in the media form a complex network. An improved understanding of breaking news diffusion characteristics can be obtained through the complex network research. Drawing on the news data of Bohai Gulf oil spill event from June 2011 to May 2014, we constructed a weighted and directed complex network in which media are set as nodes, the propagation relationships as edges and the propagation times as the weight of the edges. The primary results show (1) the propagation network presents small world feature, which means relations among media are close and breaking news originating from any node can spread rapidly; (2) traditional media and official websites are the typical sources for news propagation, while business portals are news collectors and spreaders; (3) the propagation network is assortative and the group of core media facilities the spread of breaking news faster; (4) for online media, news originality factor become less important to propagation behaviors. This study offers a new insight to explore information dissemination from the perspective of statistical physics and is beneficial for utilizing the public opinion in a positive way.

Suggested Citation

  • Liu, Nairong & An, Haizhong & Gao, Xiangyun & Li, Huajiao & Hao, Xiaoqing, 2016. "Breaking news dissemination in the media via propagation behavior based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 44-54.
  • Handle: RePEc:eee:phsmap:v:453:y:2016:i:c:p:44-54
    DOI: 10.1016/j.physa.2016.02.046
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    References listed on IDEAS

    as
    1. Mendes, G.A. & da Silva, L.R. & Herrmann, H.J., 2012. "Traffic gridlock on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 362-370.
    2. Yan, Shu & Tang, Shaoting & Pei, Sen & Jiang, Shijin & Zhang, Xiao & Ding, Wenrui & Zheng, Zhiming, 2013. "The spreading of opposite opinions on online social networks with authoritative nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3846-3855.
    3. Rainer Hegselmann & Ulrich Krause, 2002. "Opinion Dynamics and Bounded Confidence Models, Analysis and Simulation," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(3), pages 1-2.
    4. An, Haizhong & Gao, Xiangyun & Fang, Wei & Ding, Yinghui & Zhong, Weiqiong, 2014. "Research on patterns in the fluctuation of the co-movement between crude oil futures and spot prices: A complex network approach," Applied Energy, Elsevier, vol. 136(C), pages 1067-1075.
    5. Zhao, Laijun & Wang, Qin & Cheng, Jingjing & Zhang, Ding & Ma, Ting & Chen, Yucheng & Wang, Jiajia, 2012. "The impact of authorities’ media and rumor dissemination on the evolution of emergency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 3978-3987.
    6. Jia, Xiaoliang & An, Haizhong & Sun, Xiaoqi & Huang, Xuan & Gao, Xiangyun, 2016. "Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 331-344.
    7. Xiangyun Gao & Haizhong An & Weiqiong Zhong, 2013. "Features of the Correlation Structure of Price Indices," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-9, April.
    8. Chester Curme & H. Eugene Stanley & Irena Vodenska, 2015. "Coupled Network Approach To Predictability Of Financial Market Returns And News Sentiments," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 18(07), pages 1-26, November.
    9. Peng Bao & Hua-Wei Shen & Wei Chen & Xue-Qi Cheng, 2013. "Cumulative Effect in Information Diffusion: Empirical Study on a Microblogging Network," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-7, October.
    10. Sen Pei & Lev Muchnik & Shaoting Tang & Zhiming Zheng & Hernán A Makse, 2015. "Exploring the Complex Pattern of Information Spreading in Online Blog Communities," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
    11. Cristopher Moore & M. E. J. Newman, 2000. "Epidemics and Percolation in Small-World Networks," Working Papers 00-01-002, Santa Fe Institute.
    12. Katarzyna Sznajd-Weron & Józef Sznajd, 2000. "Opinion Evolution In Closed Community," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 11(06), pages 1157-1165.
    13. Bae, Joonhyun & Kim, Sangwook, 2014. "Identifying and ranking influential spreaders in complex networks by neighborhood coreness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 549-559.
    14. Rezvanian, Alireza & Rahmati, Mohammad & Meybodi, Mohammad Reza, 2014. "Sampling from complex networks using distributed learning automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 224-234.
    15. Namaki, A. & Shirazi, A.H. & Raei, R. & Jafari, G.R., 2011. "Network analysis of a financial market based on genuine correlation and threshold method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3835-3841.
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