Análisis de redes sociales y visualización yuxtapuesta de las dinámicas de opinión en Twitter a la llegada del Papa Francisco a Colombia / Analysis of social networks and juxtaposed view of the dynamics of opinion on Twitter at the arrival of Pope Francisco in Colombia
DOI:
https://doi.org/10.34117/bjdv5n7-155Keywords:
Dinámicas de opinión, comunicación digital, redes sociales, publicidad, magnetismo.Abstract
El presente estudio propone un modelo de análisis para caracterizar el movimiento observable de los agentes en las visualizaciones dinámicas de la red social Twitter desde la perspectiva física del magnetismo. Entendiendo que los modelos de dinámicas de opinión propuestos por la sociofísica recurren fundamentalmente al Modelo Ising ferromagnético, y que los algoritmos de visualización dinámica del tipo Force-Directed que traducen las redes a movimiento se basan también en principios magnéticos, se construyó un modelo de análisis bajo los mismos principios magnéticos. El modelo tiene tres descriptores magnéticos: sentido, dirección y velocidad, a los que se les suman las variables de campo magnético, temperatura y entropía. Este modelo se puso a prueba junto a la metodología clásica imágenes yuxtapuestas y análisis de redes sociales, para analizar el comportamiento de agentes comunicativos publicitarios en la conversación generada en Twitter a la llegada del Papa a Colombia. Los resultados fueron contrastados y cotejados para resaltar su utilidad en el campo publicitario.
References
Archambault, D., & Purchase, H. C. (2016). Can animation support the visualisation of dynamic graphs? Information Sciences, 330, 495–509. https://doi.org/10.1016/j.ins.2015.04.017
Bach, B., Pietriga, E., & Fekete, J. D. (2013). Graphdiaries: Animated transitions andtemporal navigation for dynamic networks. IEEE transactions on visualization and computer graphics, 20(5), 740-754. DOI: 10.1109/TVCG.2013.254
Beck, F., Burch, M., Diehl, S., & Weiskopf, D. (2014). The State of the Art in Visualizing Dynamic Graphs. Proceedings State of the Art Reports (STARs), 83–103. https://doi.org/10.2312/eurovisstar.20141174
Börner, K., Sanyal, S., & Vespignani, A. (2007). Network science. Annual Review of Information Science and Technology, 41(1), 537–607. https://doi.org/10.1002/aris.2007.1440410119
Brandes, U., & Wagner, D. (2004). Analysis and visualization of social networks. Graph Drawing Software, 1–20. https://doi.org/10.1007/3-540-45848-4_47
Cannas, S. (2010). El modelo de Ising. Curso de Mecánica Estadística, 253, 1–13.
Che, L. (2015). Laplacian-based Dynamic Graph Visualization. PacificVis 15, 69–73.
Christakis, N., & Fowler, J. (2013). Social contagion theory: Examining dynamic social networks and human behavior. Statistics in Medicine, 32(4), 1–32. https://doi.org/10.1002/sim.5408.Social
Cogan, P., Andrews, M., Bradonjic, M., Kennedy, W. S., Sala, A., & Tucci, G. (2012). Reconstruction and analysis of Twitter conversation graphs. Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research - HotSocial ’12, 25–31. https://doi.org/10.1145/2392622.2392626
Congosto, M., Basanta-Val, P., & Sanchez-Fernandez, L. (2017). T-Hoarder: A framework to process Twitter data streams. Journal of Network and Computer Applications, 83(December 2016), 28–39. https://doi.org/10.1016/j.jnca.2017.01.029
Crnovrsanin, T., Chu, J., & Ma, K. L. (2015). An incremental layout method for visualizing online dynamic graphs. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9411(1), 16–29. https://doi.org/10.1007/978-3-319-27261-0_2
Ding, Y. (2011). Community detection: Topological vs. topical. Journal of Informetrics, 5(4), 498–514. https://doi.org/10.1016/j.joi.2011.02.006
Doreian, P., Kapuscinski, R., Krackhardt, D., & Szczypula, J. (1996). A brief history of balance through time. The Journal of Mathematical Sociology. https://doi.org/10.1080/0022250X.1996.9990176
Freeman, L. (2004). The development of social network analysis. Retrieved from http://www.researchgate.net/profile/Linton_Freeman/publication/239228599_The_Development_of_Social_Network_Analysis/links/54415c650cf2e6f0c0f616a8.pdf
Fruchterman, T., & Reingold, E. (1991). Graph drawing by force directed placement. Software: Practice and Experience, 21(NOVEMBER), 1129–1164. https://doi.org/10.1002/spe.4380211102
Gaglio, S., Lo Re, G., & Morana, M. (2016). A framework for real-time Twitter data analysis. Computer Communications, 73, 236–242. https://doi.org/10.1016/j.comcom.2015.09.021
Galam, S. (2004). Sociophysics: A personal testimony. Physica A: Statistical Mechanics and Its Applications, 336(1–2), 49–55. https://doi.org/10.1016/j.physa.2004.01.009
Giddens, A., & Sutton, P. W. (2017). Conceitos essenciais da sociologia. SciELO-Editora UNESP.
Grabowski, A., & Kosi?ski, R. A. (2006). Ising-based model of opinion formation in a complex network of interpersonal interactions. Physica A: Statistical Mechanics and its Applications, 361(2), 651-664.https://doi.org/10.1016/j.physa.2005.06.102
Gwizdalla, T. M. (2008). Gallagher index for sociophysical models. Physica A: Statistical Mechanics and Its Applications, 387(12), 2937–2951. https://doi.org/10.1016/j.physa.2008.01.028
Jacomy, M., Heymann, S., Venturini, T., & Bastian, M. (2011). Force Atlas 2, a graph layout algorithm for handy network visualization. … Http://Www. Medialab. Sciences-Po. Fr/ …, 1–21. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:ForceAtlas2+,+A+Graph+Layout+Algorithm+for+Handy+Network+Visualization#0
Khan, B. S., & Niazi, M. A. (2017). Network Community Detection: A Review and Visual Survey. arXiv Preprint arXiv:1708.00977, 117(7), 1407–1425. https://doi.org/10.1108/IMDS-09-2016-0408
Khan, N., Yaqoob, I., Abaker, I., Hashem, T., Inayat, Z., Kamaleldin, W., … Gani, A. (2014). Big Data?: Survey , Technologies , Opportunities , and Challenges, 2014.
Kobourov, S. G. (2012). Spring Embedders and Force Directed Graph Drawing Algorithms, 1–23. Retrieved from http://arxiv.org/abs/1201.3011
Leonardi, S., Anagnostopoulos, A., ??cki, J., Lattanzi, S., & Mahdian, M. (2016). Community Detection on Evolving Graphs. Nips, (Nips), 3522–3530.
Moody, J., McFarland, D. A., & Bender-demoll, S. (2005). Dynamic Network Visualization. American Journal of Sociology, 110(4), 1206–1241. https://doi.org/10.1086/421509
Moore, R. (1999). The Ising Model. University of Texas, 1–27. https://doi.org/10.1017/CBO9780511804052.010
Noack, A. (2007). Unified Quality Measures for Clusterings , Layouts , and Orderings of Graphs , and Their Application as Software Design Criteria, (April 1976).
Powell, W. W., White, D. R., Koput, K. W., Smith, J. O., & Owen-smith, J. (2016). Network Dynamics and Field Evolution?: The Growth of Interorganizational Collaboration in the Life Sciences 1. American Journal of Sociology, 110(4), 1132–1205. https://doi.org/10.1086/421508
Rickles, D. (2007). Econophysics for philosophers. Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics (Vol. 38). https://doi.org/10.1016/j.shpsb.2007.01.003
Sapountzi, A., & Psannis, K. E. (2016). Social networking data analysis tools & challenges. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2016.10.019
Sazama, P. J. (2015). An Overview of Visualizing Dynamic Graphs, 1–13.
Selinger, J. V. (2016). Introduction to the Theory of Soft Matter: From Ideal Gases to Liquid Crystals. In Introduction to the Theory of Soft Matter (pp. 7–25). Springer International Publishing. https://doi.org/10.1007/978-3-319-21054-4
Shneiderman, B, & Aris, A. (2006). Network Visualization by Meaningful Substrates, 12(5), 733–740. https://doi.org/url{10.1109/TVCG.2006.166}
Stauffer, D. (2002). Sociophysics: The Sznajd model and its applications. Computer Physics Communications, 146(1), 93–98. https://doi.org/10.1016/S0010-4655(02)00439-3
Sznajd-Weron, K. (2005). Sznajd model and its applications. Acta Physica Polonica B, 36(8), 2537–2547.
Sznajd-Weron, K., & Weron, R. (2003). How effective is advertising in duopoly markets? Physica A: Statistical Mechanics and Its Applications, 324(1–2), 437–444. https://doi.org/10.1016/S0378-4371(02)01904-0
Wang, V., & Tucker, J. V. (2016). Phatic systems in digital society. Technology in Society, 46, 140–148. https://doi.org/10.1016/j.techsoc.2016.06.002
Wolfe, A. W. (1997). Social Network Analysis: Methods and Applications. American Ethnologist, 24(1), 219–220. https://doi.org/10.1525/ae.1997.24.1.219
Zhou, H., Li, J., Li, J., Zhang, F., & Cui, Y. (2017). A graph clustering method for community detection in complex networks. Physica A: Statistical Mechanics and Its Applications, 469, 551–562. https://doi.org/10.1016/j.physa.2016.11.015
