Research article

Nearcasting forwarding behaviors and information propagation in Chinese Sina-Microblog

  • Received: 19 February 2019 Accepted: 19 April 2019 Published: 11 June 2019
  • As the largest social media in China, the Sina-Microblog plays an important role in public opinion dissemination. Despite intensive efforts in understanding the information propagation dynamics, the use of a simple outbreak model to generate summative indices that can be used to characterize the time series of a single Weibo event has not been attempted. This work fills this gap, and illustrates the potential of using a simple outbreak model in conjunction with the historical data about the cumulative forwarding users for nearcasting the propagation trend.

    Citation: Fulian Yin, Xueying Shao, Jianhong Wu. Nearcasting forwarding behaviors and information propagation in Chinese Sina-Microblog[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 5380-5394. doi: 10.3934/mbe.2019268

    Related Papers:

  • As the largest social media in China, the Sina-Microblog plays an important role in public opinion dissemination. Despite intensive efforts in understanding the information propagation dynamics, the use of a simple outbreak model to generate summative indices that can be used to characterize the time series of a single Weibo event has not been attempted. This work fills this gap, and illustrates the potential of using a simple outbreak model in conjunction with the historical data about the cumulative forwarding users for nearcasting the propagation trend.


    加载中


    [1] R. Xu, How to improve the impact of traditional media microblog: based on the content analysis of People's Daily's high transmitted microblog, Practical J., 6 (2013), 15–18.
    [2] Q. Gao, F. Abel, G. J. Houben, et al., A Comparative Study of Users Microblogging Behavior on Sina Weibo and Twitter, 2012 UMAP, Springer, Berlin, Heidelberg, (2012), 88–101.
    [3] X. Shuai, X. Z. Liu, T. Xia, et al., Comparing the pulses of categorical hot events Twitter and Weibo, Proceedings of the 25th ACM Hypertext, ACM, (2014), 126–135.
    [4] X. Li, S. Cheng, W. Chen, et al., Novel user influence measurement based on user interaction in microblog, 2013 IEEE/ACM ASONAM, 615–619.
    [5] N. Zhang, Y. Chai, Y. Li, et al., Modeling micro-blog network structure based on combination of online communities, CDC, IEEE, (2013), 3419–3424.
    [6] H. Chen, Z. Xiao and C. Xin, Survey on information diffusion in microblog, Appl. Res. Comput., 31 (2014), 333–338.
    [7] H. Huo and X. Zhang, Modeling the influence of twitter in reducing and increasing the spread of influenza epidemics, SpringerPlus, 5 (2016), 88.
    [8] Y. Mei, W. Zhao and J. Yang, Influence maximization on twitter: a mechanism for effective marketing campaign, IEEE ICC, (2017), 1–6.
    [9] E. F. Can, H. Oktay and R. Manmatha, Predicting retweet count using visual cues, 2013 CIKM, ACM, (2013), 1481–1484.
    [10] F. Xiong, Y. Liu, Z. Zhang, et al., An information diffusion model based on retweeting mechanism for online social media, Phys. Lett. A, 376 (2012), 2103–2108.
    [11] D. J. Daley and D. G. Kendall, Epidemics and rumours, Nature, 4963 (1964), 1118.
    [12] J. Huang and Q. Su, A rumor spreading model based on user browsing behavior analysis in microblog, 10th ICSSSM, IEEE, (2013), 170–173.
    [13] J. Li, K. Niu, Z. He, et al., Analysis of rumor spreading in communities based on modified SIR model in microblog, 18th ICAIMSA, Springer, Cham, 8722 (2014), 69–79.
    [14] Q. Su, J. Huang and X. Zhao, An information propagation model considering incomplete reading behavior in microblog, Physica A, 419 (2015), 55–63.
    [15] J. Borge-Holthoefer, S. Meloni, B. Goncalves, et al., Emergence of influential spreaders in modified rumor models, J. Stat. Phys., 151 (2013), 383–393.
    [16] B. Wang, J. Zhang, H. Guo, et al., Model study of information dissemination in microblog community networks, Discrete Dyn. Nat. Soc., 2016 (2016), 8393016.
    [17] D. Li, X. Chen, Y. Zhan, et al., Propagation regularity of hot topics in Sina weibo based on SIR model-a simulation research, 2013 IEEE Conference Commun. Appl., IEEE, (2015), 310–315.
    [18] Y. Liu, B. Wang, B. Wu, et al., Characterizing super-spreading in microblog: an epidemic-based information propagation model, Physica A, 463 (2016), 202–218.
    [19] Y. Zhang and C. Tang, Information propagation model based on the dynamics of complex networks in mircoblogging, J. Comput. Informat. Syst., 10 (2014), 443–451.
    [20] H. Wang, Y. Li, Z. Feng, et al., Retweeting analysis and prediction in microblogs: an epidemic inspired approach, China Commun., 10 (2013), 13–24.
    [21] M. Tanaka, Y. Sakumoto, M. Aida, et al., Study on the growth and decline of SNSs by using the infectious recovery SIR model, 2015 10th APSITT, IEEE, (2015), 1–3.
    [22] D. Liu, Y.Yin and M. Song, Simulation analysis of Weibo information diffusion rule based on SIR model, JBUPT, 16 (2014), 28–33.
    [23] Y. Xiao, Y. Zhou, and S. Tan, Biological mathematics theory, in: Xi'an Jiaotong University Press, (2012).
    [24] X. Wang, J. Wu and Y. Yang, Richards model revisited: validation by and application to infection dynamics, J. Theor. Biol., 313 (2012), 12–19.
    [25] H. W. Hethcote, The mathematics of infectious diseases, SIAM Rev., 42 (2000), 599–653.
    [26] V. Karyotis and A. Khouzani, Malware diffusion models for modern complex networks: theory and applications, Morgan Kaufmann, (2015).
    [27] C. Nowzari, V. M. Preciado and G. J. Pappas, Analysis and control of epidemics: a survey of spreading processes on complex networks, IEEE Contr. Syst. Mag., 36 (2016), 26–46.
    [28] M. Freeman, J. McVittie, I. Sivak, et al., Viral information propagation in the digg online social network, Physica A, 415 (2014), 87–94.
    [29] F. Wang, H. Wang and K. Xu, Diffusive logistic model towards predicting information diffusion in online social networks, 2012 32nd ICDCS Workshops, IEEE, (2013), 133–139.
    [30] F. Wang, H. Wang, K. Xu, et al., Characterizing information diffusion in online social networks with linear diffusive model, 2013 IEEE 33rd ICDCS, IEEE, (2013), 307–316.
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(869) PDF downloads(486) Cited by(7)

Article outline

Figures and Tables

Figures(5)  /  Tables(4)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog