Abstract:To reveal the spatial characteristics of information diffusion, this paper studies the microblog information diffusion among China’s prefecture-level cities utilizing Sina microblog data, and studies the factors influencing the intercity information diffusion using gravity model. We find that a few first and second-tier cities show information monopoly and dominate the output and diffusion of microblog content. The analysis on intercity information interaction models shows that the number of users affects the intercity information diffusion to a large extent, the total GDP of cities can also predict intercity information interaction, and space distance no longer plays a part. The information diffusion in microblog breaks the limit of spatial distance. This study reveals the mapping between online social networks and offline physical space, and the intercity diffusion characteristics of information in social media, which can provide reference for spatial location-related information distribution and online public opinion monitoring.
李沧海, 许益贴, 罗春海, 胡海波. 微博信息扩散的空间分析[J]. 复杂系统与复杂性科学, 2017, 14(3): 75-84.
LI Canghai, XU Yitie, LUO Chunhai, HU Haibo. Spatial Analysis of Microblog Information Diffusion. Complex Systems and Complexity Science, 2017, 14(3): 75-84.
[1]胡海波, 王科, 徐玲, 等. 基于复杂网络理论的在线社会网络分析[J]. 复杂系统与复杂性科学, 2008, 5(2): 114. Hu Haibo, Wang Ke, Xu Ling, et al. Analysis of online social networks based on complex network theory[J]. Complex Systems and Complexity Science, 2008, 5(2): 114. [2]Hu H, Wang X. Disassortative mixing in online social networks[J]. EPL, 2009, 86: 18003. [3]Lai G, Wong O. The tie effect on information dissemination: the spread of a commercial rumor in Hong Kong[J]. Social Networks, 2002, 24(1):4975. [4]Liu Y, Wang B, Wu B, et al.Characterizing super-spreading in microblog: an epidemic-based information propagation model[J]. Physica A, 2016, 463:202218. [5]Aral S,Walker D. Identifying influential and susceptible members of social networks[J]. Nature, 2010, 466(7307):761764. [6]Dubois E, Gaffney D. The multiple facets of influence: identifying political influentials and opinion leaders on Twitter[J]. Am Behav Sci, 2014,58(10): 12601277. [7]Goel S, Anderson A, Hofman J, et al. The structural virality of online diffusion[J]. Manage Sci, 2016, 62(1): 180196. [8]刘红丽, 黄雅丽, 罗春海, 等. 基于用户行为的微博网络信息扩散模型[J]. 物理学报, 2016, 65(15): 158901. Liu Hongli, Huang Yali, Luo Chunhai, et al. Modeling information diffusion on microblog networks based on users’ behaviors[J]. Acta Phys Sin, 2016,65(15): 158901. [9]许小可, 胡海波,张伦,等. 社交网络上的计算传播学[M]. 北京: 高等教育出版社, 2015. [10] Scellato S, Noulas A, Lambiotte R, et al. Socio-spatial properties of online location-based social networks[C]// Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. Palo Alto, CA: The AAAI Press, 2011: 329336. [11] Scellato S, Mascolo C, Musolesi M, et al. Distance matters: geo-social metrics for online social networks[C]// Proceedings of the 3rd Conference on Online Social Networks. Berkeley, CA, 2010. [12] Lengyel B, Varga A, Ságvári B, et al. Geographies of an online social network[J]. PLoS One, 2015, 10(9): e0137248. [13] Leskovec J, Horvitz E. Planetary-scale views on a large instant-messaging network[C]// Proceedings of the 17th International Conference on World Wide Web. New York: ACM Press, 2008: 915924. [14] Takhteyev Y, Gruzd A, Wellman B. Geography of Twitter networks[J]. Social Networks, 2012, 34: 7381. [15] Levy M, Goldenberg J. The gravitational law of social interaction[J]. Physica A, 2014, 393: 418426. [16] KringsG, Calabrese F, Ratti C, et al. Urban gravity: a model for inter-city telecommunication flows[J]. J Stat Mech, 2009, (7): L07003. [17] Kang C, Zhang Y, Ma X, et al. Inferring properties and revealing geographical impacts of intercity mobile communication network of China using a subnet data set[J].Int J Geogr Inf Sci, 2013, 27(3): 431448. [18] 卫健炯,胡海波.在线社会网络的形成机制——基于跨学科的视角[J].复杂系统与复杂性科学,2015,12(4):1424. Wei Jianjiong, Hu Haibo.The underlyingmechanismsdrivingtheformationofonlinesocialnetworks-Interdisciplinaryperspective[J]. Complex Systems and Complexity Science,2015, 12(4): 1424. [19] Hu H, Wang X. Unified index to quantifying heterogeneity of complex networks[J]. Physica A, 2008, 387: 37693780. [20] González-BailónS, Borge-HolthoeferJ, Moreno Y. Broadcasters and hidden influentials in online protest diffusion[J]. Am Behav Sci, 2013, 57(7): 943965. [21] ZipfGK. The P1P2/D hypothesis: on the intercity movement of persons[J]. Am Sociol Rev, 1946, 11: 677686. [22] Simini F, González M C, Maritan A, et al. A universal model for mobility and migration patterns[J]. Nature, 2012, 484: 96100. [23] Liu Y, Sui Z, Kang C, et al. Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data[J]. PLoS One, 2014, 9(1): e86026. [24] van Bergeijk P A G, Brakman S. The gravity model in international trade: advances and applications[J]. Steven Brakman, 2010,19(5):979981.