New frontiers in computational social sciences: discourse, opinion, and social dynamics analysis
Keywords:
frontiers, computational social sciences:, discourse, opinion, analysis, social dynamicsSynopsis
Coordinated by Antonio Aguilera Ontiveros, who also contributes as the author of one chapter, New Frontiers in Computational Social Sciences: Discourse, Opinion, and Social Dynamics Analysis represents an innovative contribution to the study of social phenomena through computational methods. The volume integrates approaches such as agent-based simulation, text mining, and comparative analysis, applied to the examination of processes of opinion formation, cultural dissemination, and behavior on digital platforms.
Structured in six chapters, the book combines theory and practice by exploring topics ranging from corpus construction to topic analysis in recent electoral campaigns, demonstrating how the interaction between social sciences and digital tools enables a deeper understanding of contemporary issues such as polarization, social cohesion, and quality of life.
Due to its interdisciplinary nature and the leadership of its coordinator, this work becomes a key reference for researchers, educators, and students interested in applying computational approaches to social analysis, providing new methodological frameworks and critical perspectives that enrich the field of social sciences within the Latin American context.
Downloads
References
Introducción
Axelrod, R. (1997). The dissemination of culture: A model with local conver- gence and global polarization. Journal of Conflict Resolution, 41(2), 203- 226. https://doi.org/10.1177/0022002797041002001
Bankes, S. C. (2002). Agent-Based Modeling: A Revolution? Proceedings of the National Academy of Sciences of the United States of America, 99(suppl 3), 7199-7200. https://doi.org/10.1073/pnas.072081299
Blei, D. M., Ng, A. Y., y Jordan, M. I. (2003). Latent Dirichlet Allocation. Jour- nal of Machine Learning Research, 3, 993-1022. https://doi.org/10.1162/ jmlr.2003.3.4-5.993
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl 3), 7280-7287. https://doi.org/10.1073/pnas.082080899
Conte, R., Gilbert, N., Bonelli, G., y Ciampaglia, G. L. (2012). Manifesto of computational social science. epj Data Science, 1(1), 1-9. https://doi. org/10.1140/epjds4
Dusa, A. (2010). qca: A package for Qualitative Comparative Analysis in R. Journal of Statistical Software, 36(4), 1-13. https://doi.org/10.18637/jss. v036.i04
Epstein, J. M. (1999). Agent‐Based Computational Models and Genera- tive Social Science. Complexity, 4(5), 41-60. https://doi.org/10.1002/ (SICI)1099-0526(199905/06)4:5<41::AID-CPLX9>3.0.CO;2-F
Fiss, P. C. (2011). Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research. Academy of Management Journal, 54(2), 393-420. https://doi.org/10.5465/amj.2011.60263120
Grimmer, J., y Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267-297. https://doi.org/10.1093/pan/mps028
Heath, B., Hill, R., y Ciarallo, F. (2009). A Survey of Agent-Based Modeling Practices (January). Journal of Artificial Societies and Social Simulation, 12(2), 2. https://www.jasss.org/12/4/9.html.
Hopkins, D. J., y King, G. (2010). A Method of Automated Nonparametric Content Analysis for Social Science. American Journal of Political Science, 54(1), 229-247. https://tinyurl.com/y6plmzlg
Macy, M. W., y Willer, R. (2002). From Factors to Actors: Computational Sociology and Agent-Based Modeling. Annual Review of Sociology, 28, 143-166. https://doi.org/10.1146/annurev.soc.28.110601.141117
Ragin, C. C. (2006). Set Relations in Social Research: Evaluating Their Con- sistency and Coverage. Political Analysis, 14(3), 291-310. https://doi. org/10.1093/pan/mpj019
Capítulo 1
Abrica-Jacinto, N. L., y Aguilera Ontiveros, A. (2024). Influencia de la opi- nión pública en un modelo de rebelión. En A. Aguilera Ontiveros y N. L. Abrica-Jacinto (Coords.), Temas selectos para las ciencias sociales compu- tacionales: Contribuciones desde América Latina. Comunidad Editora Lati- noamericana.
Castellano, C., Fortunato, S., y Loreto, V. (2009). Statistical physics of social dynamics. Reviews of Modern Physics, 81(2), 591-646. https://doi. org/10.1103/RevModPhys.81.591
Deffuant, G., Amblard, F., Weisbuch, G., y Faure, T. (2002). How can extre- mism prevail? A study based on the relative agreement interaction model. Journal of Artificial Societies and Social Simulation, 5.
Deffuant, G., Neau, D., Amblard, F., y Weisbuch, G. (2001). Mixing beliefs among interacting agents. En Advances in Complex Systems, p. 11.
Flache, A., Mäs, M., Feliciani, T., Chattoe-Brown, E., Deffuant, G., Huet, S., y Lorenz, J. (2017). Models of social influence: Towards the next fron- tiers. Journal of Artificial Societies and Social Simulation, 20(4), 2. https://doi. org/10.18564/jasss.3521
Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Cus- tard, J., Grand, T., Heinz, S., Huse, G., Huth, A., Jepsen, J. U., Jørgensen,C., Mooij, W. M., Müller, B., Pe’er, G., Piou, C., Railsback, S. F., Robbins, A. M., Robbins, M. M., Rossmanith, E., Rüger, N., Strand, E., Souissi, S., Stillman, R. A., Vabø, R., Visser, U., y DeAngelis, D. L. (2006). A standard protocol for describing individual-based and agent-based models. Eco- logical Modelling, 198, 115-126. [doi:10.1016/j.ecolmodel.2006.04.023]
Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., y Railsback, S. F. (2010). The odd protocol: a review and first update. Ecological Modelling, 221(23), 2760-2768. [doi:10.1016/j.ecolmodel.2010.08.019]
Grimm, V., Railsback, S. F., Vincenot, C. E., Berger, U., Gallagher, C., DeAn- gelis, D. L., ..., y Ayllón, D. (2020). The odd protocol for describing agent-based and other simulation models: A second update to improve clarity, replication, and structural realism. Journal of Artificial Societies and Social Simulation, 23(2).
Hegselmann, R., Krause, U. et al. (2002). Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 5.
Helbing, D. (2012). Social Self-Organization: Agent-Based Simulations and Experiments to Study Emergent Social Behavior. Springer.
Kurmyshev, E., y Abrica-Jacinto, N. L. (2022). The effect of agents’ psychology and social environment on the opinion formation: c/pa relative agreement model in sw and sf societies. Chaos Theory and Applications, 4, 212-225.
Kurmyshev, E., Juárez, H. A., y González-Silva, R. A. (2011). Dynamics of bounded confidence opinion in heterogeneous social networks: Concord against partial antagonism. Physica A: Statistical Mechanics and its Applica- tions, 390, 2945-2955.
Lorenz, J. (2007). Continuous opinion dynamics under bounded confidence: A survey. International Journal of Modern Physics C, 18(12), 1819-1838. https://doi.org/10.1142/S0129183107011789
Miller, J. H., y Page, S. E. (2007). Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press.
Rodríguez–Zoya, L. G., y Roggero, P. (2015). Modelos basados en agentes: aportes epistemológicos y teóricos para la investigación social. Revista Mexicana de Ciencias Políticas y Sociales, 60(225), 227-261.
Wang, C. (2022). Opinion dynamics with higher-order bounded confidence. Entropy, 24, 1300.
Watts, D. J., y Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440-442.
Capítulo 2
Brunton, S. L., Proctor, J. L., y Kutz, J. N. (2016). Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 113(15), 3932-3937. https://doi.org/10.1073/pnas.1517384113
Elsenbroich, C., y Polhill, J. G. (2023). Agent-based modelling as a method for prediction in complex social systems. International Journal of Social Research Methodology, 26(2), 133-142. https://doi.org/10.1080/13645
2023.2152007
Fasel, U., Kutz, J. N., Brunton, B. W., y Brunton, S. L. (2022). Ensemble-sindy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 478(2260), 20210904. https://doi. org/10.1098/rspa.2021.0904
Ghadami, A., y Epureanu, B. I. (2022). Data-driven prediction in dynami- cal systems: Recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380(2229), 20210213. https://doi.org/10.1098/rsta.2021.0213
Giardini, F., Borit, M., Verhagen, H., y Wijermans, N. (2024). Modeling Rea- listic Human Behavior in Disasters. A Rapid Literature Review of Agent- Based Models Reviews. En C. Elsenbroich y H. Verhagen (Eds.), Advances in Social Simulation (pp. 151-162). Springer Nature Switzerland. https:// doi.org/10.1007/978-3-031-57785-7_13
Masad, D., y Kazil, J. (2015). Mesa: An Agent-Based Modeling Framework. Proceedings of the 14th Python in Science Conference, 51-58. Proceedings of the 14th Python in Science Conference. https://doi.org/10.25080/ Majora-7b98e3ed-009
Messenger, D. A., y Bortz, D. M. (2021). Weak sindy: Galerkin-Based Data- Driven Model Selection. Multiscale Modeling & Simulation, 19(3), 1474- 1497. https://doi.org/10.1137/20M1343166
Nardini, J. T., Baker, R. E., Simpson, M. J., y Flores, K. B. (2021). Learning differential equation models from stochastic agent-based model simu- lations. Journal of the Royal Society Interface, 18(176), 20200987. https:// doi.org/10.1098/rsif.2020.0987
Niemann, J.-H., Klus, S., y Schütte, C. (2021). Data-driven model reduction of agent-based systems using the Koopman generator. plos one, 16(5), e0250970. https://doi.org/10.1371/journal.pone.0250970
North, J. S., Wikle, C. K., y Schliep, E. M. (2022). A Review of Data-Driven Discovery for Dynamic Systems (arXiv:2210.10663). arXiv. http://arxiv. org/abs/2210.10663
Velázquez-Sánchez, R. D., Escobedo-Alva, J. O., Peña-García, R., Tapia-Herrera, R., y Meda-Campaña, J. A. (2024). Identification of High-Order Nonli- near Coupled Systems Using a Data-Driven Approach. Applied Sciences, 14(9), Article 9. https://doi.org/10.3390/app14093864
Wang, W.-X., Lai, Y.-C., y Grebogi, C. (2016). Data based identification and prediction of nonlinear and complex dynamical systems. Physics Reports, 644, 1-76. https://doi.org/10.1016/j.physrep.2016.06.004
Wentz, J., y Doostan, A. (2023). Derivative-based sindy (Dsindy): Addressing the challenge of discovering governing equations from noisy data. Com- puter Methods in Applied Mechanics and Engineering, 413, 116096. https:// doi.org/10.1016/j.cma.2023.116096
Wilensky, U. (1999). NetLogo. https://ccl.northwestern.edu/netlogo/
Capítulo 3
Alvarez-Llamoza, O., Cosenza, M. G., Gonzalez-Avella, J. C., Suarez, M. A., Tucci, K., y Valverde, P. (2024). Mass media competition and alternative ordering in social dynamics. Physical Review E, 110(2). https://doi. org/10.1103/physreve.110.024311
Axelrod, R. (1997). The dissemination of culture. Journal of Conflict Resolution, 41(2), 203-226. https://doi.org/10.1177/0022002797041002001
Campos Cantón, E. (2025). Orbits of families of discrete dynamical systems
evolving in the natural numbers. Chaos: An Interdisciplinary Journal of Nonlinear Science, 35(1). https://doi.org/10.1063/5.0233348
Carletti, T., Fanelli, D., Grolli, S., y Guarino, A. (2006). How to make an effi- cient propaganda. Europhysics Letters (epl), 74(2), 222-228. https://doi.org/10.1209/epl/i2005-10536-9
Centola, D., González-Avella, J. C., Eguíluz, V. M., y San Miguel, M.
(2007). Homophily, cultural drift, and the co-evolution of cultu- ral groups. Journal of Conflict Resolution, 51(6), 905-929. https://doi. org/10.1177/0022002707307632
Cosenza, M. G., Gavidia, M. E., y González-Avella, J. C. (2020). Against mass media trends: Minority growth in cultural globalization. plos one, 15(4), e0230923. https://doi.org/10.1371/journal.pone.0230923
Crokidakis, N. (2012). Effects of mass media on opinion spreading in the Sznajd sociophysics model. Physica A: Statistical Mechanics and Its Appli- cations, 391(4), 1729-1734. https://doi.org/10.1016/j.physa.2011.11.038
Flache, A., y Macy, M. W. (2006). What sustains cultural diversity and what undermines it? Axelrod and beyond. arXiv preprint physics/0604201.
González-Avella, J. C., Cosenza, M. G., y San Miguel, M. (2012). A model for cross-cultural reciprocal interactions through mass media. plos one, 7(12), e51035. https://doi.org/10.1371/journal.pone.0051035
———— (2014). Localized coherence in two interacting populations of social agents. Physica A: Statistical Mechanics and Its Applications, 399, 24-30. https://doi.org/10.1016/j.physa.2013.12.035
González-Avella, J. C., Cosenza, M. G., y Tucci, K. (2005). Nonequilibrium transition induced by mass media in a model for social influence. Physical Review E, 72(6). https://doi.org/10.1103/physreve.72.065102
Gracia-Lázaro, C., Brigatti, E., Hernández, A. R., y Moreno, Y. (2021). Pola- rization inhibits the phase transition of Axelrod’s model. Physical Review E, 103(6). https://doi.org/10.1103/physreve.103.062306
Gracia-Lázaro, C., Floría, L. M., y Moreno, Y. (2011). Selective advantage of tolerant cultural traits in the Axelrod-Schelling model. Physical Review E, 83(5). https://doi.org/10.1103/physreve.83.056103
Hernández, A. R., Gracia-Lázaro, C., Brigatti, E., y Moreno, Y. (2018). Robust- ness of cultural communities in an open-ended Axelrod’s model. Physica A: Statistical Mechanics and Its Applications, 509, 492-500. https://doi. org/10.1016/j.physa.2018.06.023
Jacobmeier, D. (2005). Multidimensional Consensus Model on a Barabási- Albert Network. International Journal of Modern Physics C, 16(04), 633- 646. https://doi.org/10.1142/s0129183105007388
Klemm, K., Eguíluz, V. M., Toral, R., y Miguel, M. S. (2003). Global culture: A noise-induced transition in finite systems. Physical Review E, 67(4). https:// doi.org/10.1103/physreve.67.045101
———— (2003). Nonequilibrium transitions in complex networks: A model of social interaction. Physical Review E, 67(2). https://doi.org/10.1103/ physreve.67.026120
Lopez Morales, S. J., González Silva, R. A., y González Silva, M. (2025). Emer- gence of multistability in the cultural dissemination of the
Axelrod model with mass media. Chaos Theory and Applications, 7(1), 78-86. https://doi. org/10.51537/chaos.1603833
Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonli- near parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2), 431-441. https://doi.org/10.1137/0111030
Raducha, T., y Gubiec, T. (2017). Coevolving complex networks in the model of social interactions. Physica A: Statistical Mechanics and Its Applications, 471, 427-435. https://doi.org/10.1016/j.physa.2016.12.079
Railsback, S. F., y Grimm, V. (2019). Agent-Based and individual-based modeling: A practical introduction, second edition. Princeton University Press.
Rodríguez, A. H., del Castillo-Mussot, M., y Vázquez, G. J. (2009). Induced monoculture in Axelrod model with clever mass media. International Journal of Modern Physics C, 20(08), 1233-1245. https://doi.org/10.1142/ s012918310901431x
Rodríguez, A. H., y Moreno, Y. (2010). Effects of mass media action on the Axelrod model with social influence. Physical Review E, 82(1). https:// doi.org/10.1103/physreve.82.016111
Shibanai, Y., Yasuno, S., e Ishiguro, I. (2001). Effects of global information feedback on diversity. Journal of Conflict Resolution, 45(1), 80-96. https:// doi.org/10.1177/0022002701045001004
Stivala, A., Kashima, Y., y Kirley, M. (2016). Culture and cooperation in a spa- tial public goods game. Physical Review E, 94(3). https://doi.org/10.1103/ physreve.94.032303
Stivala, A., Robins, G., Kashima, Y., y Kirley, M. (2014). Ultrametric distribution of culture vectors in an extended Axelrod model of cultural dissemina- tion. Scientific Reports, 4(1). https://doi.org/10.1038/srep04870
Tilles, P. F. C., y Fontanari, J. F. (2015). Diffusion of innovations in Axelrod’s model. Journal of Statistical Mechanics: Theory and Experiment, 2015(11), P11026. https://doi.org/10.1088/1742-5468/2015/11/p11026
Capítulo 4
Allcott, H., y Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211-236. doi:10.1257/ jep.31.2.211
Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Fallin Hun- zaker, M. B., ... y Volfovsky, A. (2018). Exposure to opposing views on social media can increase political polarization. Proceedings of the National Academy of Sciences, 115(37), 9216-9221. doi:10.1073/pnas.1804840115
Blei, D. M., Ng, A. Y., y Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
Casar, M. A. (2021). México: la polarización como estrategia. Revista de Ciencia Política (Santiago), 41(2), 481-507.
Dunn, W. N. (2017). Public policy analysis. Routledge.
Gelfand, A. E. (2000). Gibbs Sampling. Journal of the American Statistical Asso-
ciation, 95(452), 1300-1304. https://doi.org/10.2307/2669775 Griffiths, T. L., y Steyvers, M. (2004). Finding scientific topics. Proc Nat
Acad Sci usa, 101, 5228-5235. https://www.pnas.org/doi/epdf/10.1073/pnas.0307752101
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., y Lazer, D. (2019).
Fake news on Twitter during the 2016 US presidential election. Science, 363(6425), 374-378. doi:10.1126/science.aau2706
Grün, B., y Hornik, K. (2011). Topicmodels: An R Package for Fitting Topic Models. Journal of Statistical Software, 40(13), 1-30.
Hoffman, M. D., Blei, D. M., Wang, C., y Paisley, J. (2013). Stochastic Variational Inference. Journal of Machine Learning Research, 14, 1303-1347. Lawson, C. H. (2019). Mexico’s 2018 elections: amlo’s landslide and the transfor-
mation of Mexican democracy. Routledge
Permadi, D., y Putri, T. E. (2022). Social Media Analytics for Policy Studies. En S. Nair, y N. Varma (Eds.), Emerging Pedagogies for Policy Education. Palgrave Macmillan, Singapur. https://doi.org/10.1007/978-981-16-5864- 8_7
Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., Smyth, P., y Steyvers, M. (2010). Learning author-topic models from text corpora. acm Trans Inform Syst, 28(1), 1-38. https://doi.org/10.1145/1658377.1658381
Siklos, P. L., St. Amand, S., y Wajda, J. (2018). Appendix A: Latent Dirichlet Allocation. En The Evolving Scope and Content of Central Bank Speeches (pp. 20-20). Centre for International Governance Innovation. http://www. jstor.org/stable/resrep51882.12
Skoric, M. M., Liu, J., y Jaidka, K. (2020). Electoral and Public Opinion Fore- casts with Social Media Data: A Meta-Analysis. Information, 11(4), 187. doi: 10.3390/info11040187
Tucker, J. A., Guess, A., Barbera, P., Vaccari, C., Siegel, A., Sanovich, S., ... y Nyhan, B. (2018). Social media, political polarization, and political disin- formation: A review of the scientific literature. Political Science Research and Methods, 6(2), 215-242. doi:10.1017/psrm.2017.35
Vosoughi, S., Roy, D., y Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151. doi:10.1126/science.aap9559
Wei, X., y Croft, W. B. (2006). lda-based document models for ad-hoc retrieval. Proceedings of the international acm sigir Conference on Research and Development in Information Retrieval. Nueva York: acm, pp. 178-185. https://doi.org/10.1145/1148170.1148204
Capítulo 5
Aldas, J., y Uriel, E. (2017). Análisis multivariante aplicado con R. Paraninfo. Carlón, M. (2020). Circulación del sentido y construcción de colectivos en una sociedad hipermediatizada. Nueva Editorial Universitaria.
Fernández, J. (2018). Plataformas mediáticas. Elementos de análisis y diseño de nuevas experiencias. La Crujía.
Fraticelli, D. (2023). El humor hipermediático. Una nueva era de la mediatización reidera. Teseo.
Gindin, I., y Busso, M. (2018). Investigaciones en comunicación en tiempos de big data: sobre metodologías y temporalidades en el abordaje de redes sociales. adComunica. Revista Científica de Estrategias, Tendencias e Innovación en Comunicación, (15), 25-43. https://www.raco.cat/index.php/adComu- nica/article/view/343017
Gindin, I., y Raimondo Anselmino, N. (2024). Mediatizaciones del discurso sobre lo público-común plataformizado: estudio interdisciplinario del “narcoterrorismo” en la ciudad de Rosario (Argentina). En I. Gindin y M. C. Reviglio, La mediatización insomne. 40 años de sueños y pesadillas demo- cráticas (pp. 78-96). unr Editora. https://cim.unr.edu.ar/publicaciones/1/ libros/181/la-mediatizacion-insomne-40-anos-de-suenos-y-pesadillas- democraticas
Kataishi, R., y Milia, M. (2024). El análisis semántico-estadístico como estra- tegia de abordaje metodológico: reflexiones sobre su pertinencia en el estudio de problemáticas latinoamericanas. En J. M. Natera y D. Suárez (Comps.), Métodos para el análisis de los procesos de ciencia, tecnología e inno- vación: herramientas para el estudio del desarrollo de América Latina. Métodos mixtos y emergentes, vol. 3. Ediciones ungs.
Kerbrat-Orecchioni, K. (1996). La conversation. Seuil. Traducción al español: Preiti, M. Cátedra de Lengua Española ii, Facultad de Humanidades y Artes, unr.
Mantilla-Valbuena, S. (2008). Más allá del discurso hegemónico: narcotráfico, terrorismo y narcoterrorismo en la era del miedo y la inseguridad global. Papel Político, 13(1), 227-259.
Marradi, A., Archenti, N., y Piovani, J. I. (2018). Manual de metodología de las ciencias sociales. Siglo XXI.
Martín-Crespo Blanco, C., y Salamanca Castro, A. (2007). El muestreo en la investigación cualitativa. nure Investigación: Revista Científica de Enfermería, (27).
Montes del Castillo, Á., y Montes Martínez, A. (2014). Guía para proyectos de investigación. Universitas, 12(20), 91-126.
Raimondo Anselmino, N. (2011). O ocaso do modelo intencional: a noção de “estratégia discursiva” sob o olhar sócio-semiótico. Semeiosis: Semiótica e Transdisciplinaridade em Revista, 1(2). https://semeiosis.com.br/issues?i ssue=YEv5nsst2sPcL29M5KWG&article=EaGHosEF8L5DVptfXaWC
———— (2013). Un análisis sociosemiótico de la prensa online: investigar el presente en transición. En N. Raimondo Anselmino y M. C. Reviglio (Eds.), Territorios de comunicación. Recorridos de investigación para abordar un campo heterogéneo. Quipus, Ciespal.
———— (25 de julio de 2022). Semiodata: una estrategia combinada para el estudio de la producción de sentido en el estadio actual de la mediatización. Conferencia realizada en el marco del Seminario “Arqueologías de la mediatización. Tiempos, espacios y
tecnologías del mundo actual”. https://www.you- tube.com/watch?v=7rP8k6YpHvo
Raimondo Anselmino, N., Cardoso, A. L., Rostagno, J., y Sambrana, A. (2019). Recursos paratextuales y paralingüísticos en las fanpages de los periódicos argentinos Clarín y La Nación. Atributos del discurso de la prensa en las redes. Perspectivas de la Comunicación, 2(2), 245-280. https://www.pers- pectivasdelacomunicacion.cl/index.php/perspectivas/article/view/2028
Raimondo Anselmino, N., Reviglio, M. C., y Diviani, R. (2015). Esfera pública y redes sociales en Internet: ¿Qué es lo nuevo en Facebook? Revista Mediterránea de Comunicación, 7(1).
van Dijck, J., Poell, T., y de Waal, M. (2018). The Platform Society. Public values in a connective world. Oxford University Press.
Verón, E. (1998). La semiosis social. Fragmentos de una teoría de la discursividad. Gedisa.
———— (2013). La semiosis social, 2. Ideas, momentos, interpretantes. Paidós.
Capítulo 6
Ardila, R. (2003). Calidad de vida: una definición integradora. Revista Lati- noamericana de Psicología, 35(2), 161-164.
Banco Mundial (2024). pib per cápita (usd actuales). https://data.worldbank. org/indicator/NY.GDP.PCAP.CD
Bronfenbrenner, U., y Morris, P. A. (2006). The bioecological model of human development. En R. M. Lerner (Ed.), Handbook of child psychology (6.a ed., vol. 1, pp. 793-828). John Wiley & Sons.
Datos Mundial (2024). Comparación de calidad de vida por país. https:// www.datosmundial.com/calidad-de-vida.php
Eustat (2024). Índice de desarrollo humano por indicadores según países 2024. Eustat-Instituto Vasco de Estadística. https://www.eustat.eus/ele- mentos/ele0013500/ti_indice-de-desarrollo-humano-por-indicadores- segun-paises-2019/tbl0013566_c.html
Felce, D., y Perry, J. (1995). Quality of life: Its definition and measurement. Research in Developmental Disabilities, 16, 51-74.
Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typo- logies in organization research. Academy of Management Journal, 54(2), 393-420.
Govea, A. M. (2018). Evolución e impacto del pib y el idh en un mundo desigual. Revista Vinculando, 16(1).
Helliwell, J. F., Layard, R., Sachs, J. D., Aknin, L. B., De Neve, J.-E., y Wang, S. (Eds.). (2023). World Happiness Report 2023 (11.a ed.). Sustainable Development Solutions Network. https://worldhappiness.report/ ed/2023/#appendices-and-data
Kahneman, D., y Deaton, A. (2010). High income improves evaluation of life but not emotional well-being. Proceedings of the National Academy of Sciences, 107(38), 16489-16493.
Legewie, N. (2013). An Introduction to Applied Data Analysis with Qualitative Comparative Analysis. Deutsches Institut für Wirtschaftsforschung.
Medina, I., Álamos-Concha, P., Castillo Ortiz, P. J., y Rihoux, B. (2017). Aná- lisis cualitativo comparado (qca) (vol. 56). cis-Centro de Investigaciones Sociológicas.
Mejía-Trejo, J. (2021). Análisis Cualitativo Comparativo (fsqca) y su relación con la innovación: Discusión e interpretación de resultados (tomo ii). Universidad de Guadalajara.
Nicolás, S. C. M., Alejandro, A. H. S., Gabriel, R. C. E., y Katherine, R. A. C. (2022). Calidad de vida: el camino de la objetividad a la subjetividad en población general y grupos como: niños y jóvenes, personas con disca- pacidad y adultos mayores. Revista Médica Vozandes, 33(1), 61.
Organización para la Cooperación y el Desarrollo Económicos (ocde) (2022). ¿Cómo va la vida en América Latina? Medición del bienestar para la formulación de políticas públicas. oecd Publishing. https://doi.org/10.1787/7f6a948f-es
———— (2024). Índice para una vida mejor. https://www.oecdbetterlifeindex. org/es/
Ornelas, A. R., y Ruíz, A. M. (2017). Salud mental y calidad de vida: Su relación en los grupos etarios. psiencia. Revista Latinoamericana de Ciencia Psicológica, 9(2), 1-16.
Palomba R. (2002). Calidad de Vida: Conceptos y medidas. Documentos del Taller sobre calidad de vida y redes de apoyo de las personas adultas mayores. Cen- tro Latinoamericano y Caribeño de Demografía (celade) / División de Población, Comisión
Económica para América Latina y el Caribe (cepal). Fondo de Población de las Naciones Unidas, julio de 2002.
Ragin, C. (2008). Resigning social Inquiry. Fuzzy Set and Beyond. The University of Chicago Press.
Salas, C., y Garzón, M. O. (2013). La noción de calidad de vida y su medición. Revista ces Salud Pública, 4(1), 36-46.
Schneider, C., y Wagemand C. (2012). Set Theoretic Methods for the Social Sciences. A guide to Qualitative Comparative Analysis. Cambridge.
Social Progress Imperative (2024). Global Social Progress Index. https://www. socialprogress.org/social-progress-index
Tonon, G. (2010). La utilización de indicadores de calidad de vida para la decisión de políticas públicas. Polis. Revista Latinoamericana, (26).
Urzúa, A., y Caqueo-Urízar, A. (2012). Calidad de vida: Una revisión teórica del concepto. Terapia Psicológica, 30(1), 61-71.
Urzúa, A., Pavlov, R., Cortés, R., y Pino, V. (2011). Factores psicosociales rela- cionados con la calidad de vida en salud en pacientes hemodializados. Terapia Psicológica, 29(1), 135-140.
Varela, L. F. (2016). Salud y calidad de vida en el adulto mayor. Revista Peruana de Medicina Experimental y Salud Pública, 33, 199-201.
Veenhoven, R. (2000). The four qualities of life: Ordering concepts and mea- sures of the good life. Journal of Happiness Studies, 1(1), 1-39.
Downloads
Published
Categories
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

