Abstract
It is crucial to discover, track, summarize, and even predict popular topics and events occurring in the social network in the space-time context. At the same time, it is very useful that a series of “what if” scenarios can be developed to estimate the meme diffusion. However, spatial social scientists have been slow to adopt and implement new methods for social media data analysis due to the lack of open source software packages, which become a major impediment to the promotion of human dynamics research. The availability and widespread use of source codes will play a critical role in the adoption of new perspectives and ideas enhancing spatial social network analytics. The proposed Open Source Social Network Simulator implements the methodological advances in an open source environment of Python for exploring spatial meme diffusion, using twitter data as the case study. The methods are built in open source environments and thus are easily extensible and customizable. The open source movement can also facilitate the explosion of the social media analytics routines by increasingly easier development processes with powerful scripting language environments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Allaway, A. W., Berkowitz, D., & D’Souza, G. (2003). Spatial diffusion of a new loyalty program through a retail market. Journal of Retailing, 79(3), 137–151.
Allaway, A. W., Black, W. C., Richard, M. D., & Mason, J. B. (1994). Evolution of a retail market area: An event history model of spatial diffusion. Economic Geography, 70(1), 23–40.
Bailey, N. (1975). The mathematical theory of infectious diseases and its applications. London: Charles Griffin & Company Ltd.
Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012, April). The role of social networks in information diffusion. In Proceedings of the 21st International Conference on World Wide Web (pp. 519–528). ACM.
Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.
Barrat, A., & Weigt, M. (2000). On the properties of small-world network models. The European Physical Journal B-Condensed Matter and Complex Systems, 13(3), 547–560.
Baybeck, B., & Huckfeldt, R. (2002). Urban contexts, spatially dispersed networks, and the diffusion of political information. Political Geography, 21(2), 195–220.
Budak, C., Agrawal, D., & Abbadi, A. E. (2011). Limiting the spread of misinformation in social networks. In Proceedings of the 20th International Conference on World Wide Web (WWW ’11).
Cao, N., et al. (2012). Whisper: tracing the spatiotemporal process of information diffusion in real time. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2649–2658.
Centola, D. (2010). The spread of behavior in an online social network experiment. Science, 329(5996), 1194–1197.
Chesterman, A. (2016). Memes of translation: The spread of ideas in translation theory (Vol. 123). Amsterdam: John Benjamins Publishing Company.
Clauset, A., Newman, M. E., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70(6), 066111.
Doo, M. (2012). Spatial and social diffusion of information and influence: Models and algorithms (Doctoral dissertation, Georgia Institute of Technology).
Erdös, P., & Rényi, A. (1960). On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences, 5(17–61), 43.
Granovetter, M. S. (1987). Threshold models of collective behavior. American Journal of Sociology, 83(6), 1420–1443.
Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826.
Goldenberg, J., Libai, B., & Muller, E. (2001). Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12(3), 211–223.
Gregory, D., & Urry, J. (1985). Suspended animation: The stasis of diffusion theory. In D. Gregory & J. Urry (Eds.), Social relations and spatial structures (pp. 296–336). New York: St. Martin’s Press.
Guille, A., & Hacid. H. (2012). A predictive model for the temporal dynamics of information diffusion in online social networks. In WWW ’12 Companion (pp. 1145–1152).
Hägerstrand, T. (1967). Aspects of the spatial structure of social, communication and the diffusion of information. Papers in Regional Science, 16(1), 27–42.
Hanneman, R. E. (2000). Introduction to social network methods. Online textbook supporting sociology 157. Riverside, CA: University of California.
Hu, Y., Ye, X., & Shaw, S. L. (2017). Extracting and analyzing semantic relatedness between cities using news articles. International Journal of Geographical Information Science, 31(12), 2427–2451.
Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’03).
Knoke, D. (1982). The spread of municipal reform: Temporal, spatial, and social dynamics. American Journal of Sociology, 87(6), 1314–1339.
Lancichinetti, A., Radicchi, F., Ramasco, J. J., & Fortunato, S. (2011). Finding statistically significant communities in networks. PLoS ONE, 6(4), e18961.
Lee, J., Lay, J. G., Chin, W. C. B., Chi, Y. L., & Hsueh, Y. H. (2014). An experiment to model spatial diffusion process with nearest neighbor analysis and regression estimation. International Journal of Applied Geospatial Research, 5(1), 1–15.
Lee, J., & Ye, X. (2018). An open source spatiotemporal model for simulating obesity prevalence. In GeoComputational Analysis and Modeling of Regional Systems (pp. 395–410). Cham: Springer.
Liang, Y., Caverlee, J., Cheng, Z., & Kamath, K. Y. (2013). How big is the crowd? Event and location based population modeling in social media. In Proceedings of the 24th ACM Conference on Hypertext and Hypermedia. Paris, France.
Li, M., Ye, X., Zhang, S., Tang, X., & Shen, Z. (2017). A framework of comparative urban trajectory analysis. Environment and Planning B. https://doi.org/10.1177/2399808317710023.
Li, S., Ye, X., Lee, J., Gong, J., & Qin, C. (2016). Spatiotemporal analysis of housing prices in China: A big data perspective. Applied Spatial Analysis and Policy. https://doi.org/10.1007/s12061-016-9185-3.
Morrill, R., Gaile, G. L., & Thrall, G. I. (1988). Spatial diffusion. SAGE scientific geography series 10. Newbury Park, CA: SAGE Publications, Inc.
Newman, M. E. (2008). The mathematics of networks. The New Palgrave Encyclopedia of Economics, 2(2008), 1–12.
Newman, M. E. J. (2003). The structure and function of complex networks. Society for Industrial and Applied Mathematics (SIAM) Review, 45(2), 167–256.
Newman, M. E. J., Forrest, S., & Balthrop, J. (2002). Email networks and the spread of computer viruses. Physical Review, 66(3), 035101.
Romero, D. M., Tan. C., & Ugander, J. (2013). On the interplay between social and topical structure. In Proceedings of AAAI International Conference on Weblogs and Social Media (pp. 516–525).
Romero, D. M., Galuba, W., Asur, S., & Huberman, B. A. (2011, March). Influence and passivity in social media. In Proceedings of the 20th international conference companion on World wide web (pp. 18–33). ACM.
Scott, J. (2012). Social network analysis. UK: Sage.
Shaw, S., Tsou, M., & Ye, X. (2016). Human dynamics in the mobile and big data era. International Journal of Geographical Information Science, 30(9), 1687–1693.
Spitzberg, B. H. (2014). Toward a model of meme diffusion (M3D). Communication Theory, 24(3), 311–339.
Sui, D. (2014). Opportunities and impediments for open GIS. Transactions in GIS, 18(1), 1–24.
Wang, F., Wang, H., & Xu, K. (2012). Diffusive logistic model towards predicting information diffusion in online social networks. In ICDCS ’12 Workshops (pp. 133–139).
Wang, S. (2016). CyberGIS and spatial data science. GeoJournal. https://doi.org/10.1007/s10708-016-9740-0.
Wang, Y. D., Fu, X. K., Jiang, W., Wang, T., Tsou, M. H., & Ye, X. (2017). Inferring urban air quality based on social media. Computers, Environment and Urban Systems, 66, 110–116.
Wang, Z., & Ye, X. (2017). Social media analytics for natural disaster management. International Journal of Geographical Information Science. https://doi.org/10.1080/13658816.2017.1367003.
Wang, Z., Ye, X., & Tsou, M. (2016). Spatial, temporal, and content analysis of Twitter for wildfire hazards. Natural Hazards. https://doi.org/10.1007/s11069-016-2329-6.
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.
Weng, L., Menczer, F., & Ahn, Y.-Y. (2013). Virality prediction and community structure in social networks. Scientific Reports, 3, 2522.
Yang, J., & Counts, S. (2010). Predicting the speed, scale, and range of information diffusion in Twitter. In 4th International AAAI Conference on Weblogs and Social Media (ICWSM).
Yang, J., & Leskovec, J. (2010). Modeling information diffusion in implicit networks. In ICDM ’10: IEEE International Conference On Data Mining, 2010.
Ye, X. (2017). Open data and open source GIS, In Huang, B. (Ed.), Comprehensive geographic information systems (Vol. 1, pp. 42–49). Oxford: Elsevier. https://doi.org/10.1016/b978-0-12-409548-9.09592-0.
Ye, X., Li, S., Yang, X., Lee, J., & Wu, L. (2018). The fear of Ebola: A tale of two cities in China. In Big data support of urban planning and management (pp. 113–132). Cham: Springer.
Ye, X., & Lee, J. (2016). Integrating geographic activity space and social network space to promote healthy lifestyles. ACMSIGSPATIAL Health GIS., 8(1), 24–33.
Ye, X., & Rey, S. J. (2013). A framework for exploratory space-time analysis of economic data. Annals of Regional Science, 50(1), 315–339.
Zhang, J., Liu, X., & Tong, Z. (2012). Natural disaster risk assessment using information diffusion and geographical information system. In: Lu, J., Jain, L., & Zhang, G. (Eds.), Handbook on decision making (pp. 309–330). Berlin, Heidelberg: Springer.
Acknowledgements
This material is based upon work supported by the National Science Foundation under Grant No. 1416509 and 1637242.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Ye, X., Dang, L., Lee, J., Tsou, MH., Chen, Z. (2018). Open Source Social Network Simulator Focusing on Spatial Meme Diffusion. In: Shaw, SL., Sui, D. (eds) Human Dynamics Research in Smart and Connected Communities. Human Dynamics in Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-319-73247-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-73247-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73246-6
Online ISBN: 978-3-319-73247-3
eBook Packages: Social SciencesSocial Sciences (R0)