Εμφανίζονται 1 - 12 Αποτελέσματα από 12 για την αναζήτηση '"SOCIAL NETWORKS USERS"', χρόνος αναζήτησης: 0,64δλ Περιορισμός αποτελεσμάτων
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    Academic Journal

    Συγγραφείς: Kondratyevа, O. N.

    Πηγή: Филологический класс, Iss 1, Pp 62-72 (2020)

    Θεματικοί όροι: media discourse, КОНТЕНТ-АНАЛИЗ, RUSSIAN LANGUAGE, социальные сети, ПОЛЬЗОВАТЕЛИ СОЦИАЛЬНЫХ СЕТЕЙ, СРЕДСТВА МАССОВОЙ ИНФОРМАЦИИ, МЕТАФОРИЧЕСКИЕ НОМИНАЦИИ, медиалингвистика, MEDIA DISCOURSE, media texts, медиадискурс, МЕТАФОРИЧЕСКОЕ МОДЕЛИРОВАНИЕ, METAPHORICAL MODELING, метафорическое моделирование, интернет-дискурс, ТИПЫ ПОВЕДЕНИЯ, ЛИНГВИСТИКА ТЕКСТА, internet discourse, КОНЦЕПТУАЛЬНЫЕ МЕТАФОРЫ, 05 social sciences, русский язык, social networks users, СОЦИАЛЬНЫЕ СЕТИ, ИНТЕРНЕТ-ПОВЕДЕНИЕ, conceptual metaphors, metaphorical models, РОССИЯ, ИНТЕРНЕТ, ИНТЕРНЕТ-КОММУНИКАЦИИ, INTERNET DISCOURSE, РУССКИЙ ЯЗЫК, концептуальные метафоры, social networks, РОССИЙСКИЕ СМИ, CONCEPTUAL METAPHORS, СМИ, метафорические модели, ПОВЕДЕНЧЕСКИЕ ХАРАКТЕРИСТИКИ, P1-1091, media linguistics, МЕТАФОРИЧЕСКАЯ ЭКСПАНСИЯ, russian language, ТИПОЛОГИИ ПОЛЬЗОВАТЕЛЕЙ, МЕДИАТЕКСТЫ, METAPHORICAL MODELS, 0502 economics and business, metaphorical modeling, ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ, ИНТЕРНЕТ-ПОЛЬЗОВАТЕЛИ, Philology. Linguistics, MEDIA LINGUISTICS, 0505 law, ЯЗЫКОВЫЕ СРЕДСТВА, ТИПЫ ПОЛЬЗОВАТЕЛЕЙ, медиатексты, МЕДИАЛИНГВИСТИКА, Russian language, пользователи социальных сетей, SOCIAL NETWORKS USERS, МЕДИАДИСКУРС, МЕТАФОРИЧЕСКИЕ МОДЕЛИ, ИНТЕРНЕТ-ДИСКУРС, SOCIAL NETWORKS, ИНТЕРНЕТ-ТЕКСТЫ, MEDIA TEXTS, ЯЗЫКОЗНАНИЕ

    Περιγραφή αρχείου: application/pdf

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    eBook

    Συνεισφορές: Alhajj, Reda, editorAff1, Rokne, Jon, editorAff2

    Πηγή: Encyclopedia of Social Network Analysis and Mining. :2760-2760

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    eBook

    Συνεισφορές: Alhajj, Reda, editorAff1, Rokne, Jon, editorAff2

    Πηγή: Encyclopedia of Social Network Analysis and Mining. :1918-1918

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    Dissertation/ Thesis

    Συγγραφείς: Álvarez Monroy, Victor Nicolás

    Συνεισφορές: Sosa Martínez, Juan Camilo, orcid:0000-0001-7432-4014, https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000019698, Universidad Santo Tomás

    Θέμα γεωγραφικό: CRAI-USTA Bogotá

    Περιγραφή αρχείου: application/pdf

    Relation: Albert, J. H., & Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American statistical Association, 88(422), 669-679.; Aldous, D. J. (1985). Exchangeability and related topics. In École d'Été de Probabilités de Saint-Flour XIII—1983 (pp. 1-198). Springer, Berlin, Heidelberg.; Aliverti, E., & Russo, M. (2020). Stratified stochastic variational inference for high-dimensional network factor model. arXiv preprint arXiv:2006.14217.; Banerjee, A., Chandrasekhar, A. G., Duflo, E., & Jackson, M. O. (2013). The diffusion of microfinance. Science, 341(6144).; Borg, I., & Groenen, P. J. (2005). Modern multidimensional scaling: Theory and applications. Springer Science & Business Media.; D'Angelo, S., Alfò, M., & Fop, M. (2020). Model-based Clustering for Multivariate Networks. arXiv preprint arXiv:2001.05260.; D'Angelo, S., Alfò, M., & Murphy, T. B. (2018, May). Node-specific effects in latent space modelling of multidimensional networks. In 49th Scientific meeting of the Italian Statistical Society.; Durante, D., & Dunson, D. B. (2014). Nonparametric Bayes dynamic modelling of relational data. Biometrika, 101(4), 883-898.; Durante, D., & Dunson, D. B. (2018). Bayesian inference and testing of group differences in brain networks. Bayesian Analysis, 13(1), 29-58.; D’Angelo, S., Murphy, T. B., & Alfò, M. (2019). Latent space modelling of multidimensional networks with application to the exchange of votes in eurovision song contest. Annals of Applied Statistics, 13(2), 900-930.; Gamerman, D., & Lopes, H. F. (2006). Markov chain Monte Carlo: stochastic simulation for Bayesian inference. CRC Press.; Gao, L. L., Witten, D., & Bien, J. (2019). Testing for Association in Multi-View Network Data. arXiv preprint arXiv:1909.11640.; Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. CRC press.; Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and computing, 24(6), 997-1016.; Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical science, 7(4), 457-472.; Gollini, I., & Murphy, T. B. (2016). Joint modeling of multiple network views. Journal of Computational and Graphical Statistics, 25(1), 246-265.; Green, P. J., & Hastie, D. I. (2009). Reversible jump MCMC. Genetics, 155(3), 1391-1403.; Gupta, S., Sharma, G., & Dukkipati, A. (2018). Evolving Latent Space Model for Dynamic Networks. arXiv preprint arXiv:1802.03725.; Haario, H., Saksman, E., & Tamminen, J. (2001). An adaptive Metropolis algorithm. Bernoulli, 7(2), 223-242.; Han, Q., Xu, K., & Airoldi, E. (2015, June). Consistent estimation of dynamic and multi-layer block models. In International Conference on Machine Learning (pp. 1511-1520).; Handcock, M. S., Raftery, A. E., & Tantrum, J. M. (2007). Model‐based clustering for social networks. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(2), 301-354.; Hoff, P. (2008). Modeling homophily and stochastic equivalence in symmetric relational data. In Advances in neural information processing systems (pp. 657-664).; Hoff, P. D. (2005). Bilinear mixed-effects models for dyadic data. Journal of the american Statistical association, 100(469), 286-295.; Hoff, P. D. (2009). A first course in Bayesian statistical methods (Vol. 580). New York: Springer.; Hoff, P. D. (2015). Multilinear tensor regression for longitudinal relational data. The annals of applied statistics, 9(3), 1169.; Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the american Statistical association, 97(460), 1090-1098.; Hoover, D. N. (1982). Row-column exchangeability and a generalized model for probability. Exchangeability in probability and statistics (Rome, 1981), 281-291.; Kim, B., Lee, K. H., Xue, L., & Niu, X. (2018). A review of dynamic network models with latent variables. Statistics surveys, 12, 105.; Kolaczyk, E. D., & Csárdi, G. (2014). Statistical analysis of network data with R (Vol. 65). New York, NY: Springer.; Krackhardt, D. (1987). Cognitive social structures. Social networks, 9(2), 109-134.; Handcock, M. S., & Krivitsky, P. N. (2008). Fitting Latent Cluster Models for Networks with latentnet. Journal of Statistical Software, 24(05).; Krivitsky, P. N., Handcock, M. S., Raftery, A. E., & Hoff, P. D. (2009). Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models. Social networks, 31(3), 204-213.; Li, W. J., Yeung, D. Y., & Zhang, Z. (2011). Generalized latent factor models for social network analysis. In Proceedings of the 22nd international joint conference on artificial intelligence (ijcai), barcelona, spain (p. 1705).; Linkletter, C. D. (2007). Spatial process models for social network analysis (Doctoral dissertation, Simon Fraser University).; Ma, Z., & Ma, Z. (2017). Exploration of large networks with covariates via fast and universal latent space model fitting. arXiv preprint arXiv:1705.02372.; Minhas, S., Hoff, P. D., & Ward, M. D. (2019). Inferential approaches for network analysis: AMEN for latent factor models. Political Analysis, 27(2), 208-222.; Nowicki, K., & Snijders, T. A. B. (2001). Estimation and prediction for stochastic blockstructures. Journal of the American statistical association, 96(455), 1077-1087.; Paez, M. S., Amini, A. A., & Lin, L. (2019). Hierarchical stochastic block model for community detection in multiplex networks. arXiv preprint arXiv:1904.05330.; Paul, S., & Chen, Y. (2016). Consistent community detection in multi-relational data through restricted multi-layer stochastic blockmodel. Electronic Journal of Statistics, 10(2), 3807-3870.; Paul, S., & Chen, Y. (2020). Spectral and matrix factorization methods for consistent community detection in multi-layer networks. The Annals of Statistics, 48(1), 230-250.; Polson, N. G., Scott, J. G., & Windle, J. (2013). Bayesian inference for logistic models using Pólya–Gamma latent variables. Journal of the American statistical Association, 108(504), 1339-1349.; Raftery, A. E., Niu, X., Hoff, P. D., & Yeung, K. Y. (2012). Fast inference for the latent space network model using a case-control approximate likelihood. Journal of Computational and Graphical Statistics, 21(4), 901-919.; Reyes, P., & Rodriguez, A. (2016). Stochastic blockmodels for exchangeable collections of networks. arXiv preprint arXiv:1606.05277.; Roethlisberger, F. J., & Dickson, W. J. (2003). Management and the Worker (Vol. 5). Psychology press.; Salter-Townshend, M., & McCormick, T. H. (2017). Latent space models for multiview network data. The annals of applied statistics, 11(3), 1217.; Schweinberger, M., & Snijders, T. A. (2003). Settings in social networks: A measurement model. Sociological Methodology, 33(1), 307-341.; Sewell, D. K., & Chen, Y. (2015). Latent space models for dynamic networks. Journal of the American Statistical Association, 110(512), 1646-1657.; Sewell, D. K., & Chen, Y. (2016). Latent space models for dynamic networks with weighted edges. Social Networks, 44, 105-116.; Sewell, D. K., & Chen, Y. (2017). Latent space approaches to community detection in dynamic networks. Bayesian Analysis, 12(2), 351-377.; Sewell, D. K. (2019). Latent space models for network perception data. Netw. Sci., 7(2), 160-179.; Sosa, J. (2017). A Latent Space Approach for Cognitive Social Structures Modeling and Graphical Record Linkage (Doctoral dissertation, UC Santa Cruz).; Spencer, N. A., Junker, B., & Sweet, T. M. (2020). Faster MCMC for Gaussian Latent Position Network Models. arXiv preprint arXiv:2006.07687.; Swartz, T. B., Gill, P. S., & Muthukumarana, S. (2015). A Bayesian approach for the analysis of triadic data in cognitive social structures. Journal of the Royal Statistical Society: Series C: Applied Statistics, 593-610.; Turnbull, K. (2020). Advancements in latent space network modelling (Doctoral dissertation, Lancaster University).; Wang, L., Zhang, Z., & Dunson, D. (2019). Common and individual structure of brain networks. The Annals of Applied Statistics, 13(1), 85-112.; Watanabe, S., & Opper, M. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of machine learning research, 11(12).; Zhang, X. (2020). Statistical Analysis for Network Data using Matrix Variate Models and Latent Space Models (Doctoral dissertation).; Alvarez Monroy, V.N. (2021). Modelamiento de redes sociales múltiples. [Tesis de maestría, Universidad Santo Tomás Colombia]. Repositorio Institucional; http://hdl.handle.net/11634/31872; reponame:Repositorio Institucional Universidad Santo Tomás; instname:Universidad Santo Tomás; repourl:https://repository.usta.edu.co

    Διαθεσιμότητα: http://hdl.handle.net/11634/31872

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    Dissertation/ Thesis

    Συγγραφείς: Rivera Agredo, Ferney Mauricio

    Συνεισφορές: Mahecha Sánchez, Gloria Andrea

    Θέμα γεωγραφικό: Socorro

    Περιγραφή αρχείου: PDF

    Relation: Real Academia Española. (s.f.). Red social. En Diccionario de la lengua española. Recuperado el 11 de diciembre de 2020, de https://dpej.rae.es/lema/red-social https://www.oas.org/dil/esp/tratados_b32_convencion_americana_sobre_derechos_humanos.htm; Arab, L. E., & Díaz, G. A. (2015). Impacto de las redes sociales e internet en la adolescencia: aspectos positivos y negativos. Revista Médica Clínica Las Condes, 26(1), 7-13.; ARREAZA. (27 DE MAYO DE 2020). Historia de Internet en Colombia: cómo evolucionó la red de redes en nuestro país. Marketing Ecommerce. Recuperado de https://marketing4ecommerce.co/historia-de-internet-en-colombia/; DE LA HERA (30 de junio de 2020). BREVE HISTORIA DE LAS REDES [IMAGEN]. RECUPERADO DE https://s3.amazonaws.com/cdn.wp.m4ecnet/wp-content/uploads/2019/10/17172334/historia-de-las-redes-sociales.jpg; Convención americana sobre derechos humanos (1969) Articulo 13 [Capitulo 2] Recuperado de: https://www.oas.org/dil/esp/tratados_b32_convencion_americana_sobre_derechos_humanos.htm; Corte Constitucional. (2 de junio de 2016) Sentencia T-5.350.821. [M.P. ALBERTO ROJAS RÍOS]; Corte Constitucional. (3 de diciembre de 2013) Sentencia T-3982238. [M.P. MARÍA VICTORIA CALLE CORREA]; Corte Constitucional. (13 de abril de 2016) Sentencia D-11007. [M.P. GLORIA STELLA ORTIZ DELGADO]; Constitución política de Colombia [Const.] (1991) Artículo 44 [Titulo II]. Recuperado de: http://www.secretariasenado.gov.co/senado/basedoc/constitucion_politica_1991.html; Código de la infancia y adolescencia [Código] (2006), Ley 1098 de 2006, Articulo 13 [Titulo 2, Capitulo 1] (colombia) Recuperado de: http://www.secretariasenado.gov.co/senado/basedoc/ley_1098_2006.html#38; Convención sobre los derechos del niño (20 de noviembre de 1989) Articulo 5 [Parte 1] Recuperado de: https://www.un.org/es/events/childrenday/pdf/derechos.pdf; Góchez, Rafael Francisco. Octubre de 2009. Los riesgos de las redes sociales virtuales. www.externado.edu.sv/index.php?option=com_content&view=article&id=78:los-riesgos-de-lasredes-sociales-virtuales; https://hdl.handle.net/10901/19447

    Διαθεσιμότητα: https://hdl.handle.net/10901/19447

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    Academic Journal

    Relation: Vestnik of St Petersburg University. Psychology and Education;Volume 7; Issue 4; Ledovaya Ya. A., Tikhonov R. V., Ivanov V. Yu., Yaminov B. R., Bogolyubova O. N. Organisational and methodological issues of data collection in an internet based study of Facebook users from Russia and USA. Vestnik SPbSU. Psychology and Education, 2017, vol. 7, issue 4, pp. 308–327.; http://hdl.handle.net/11701/9125

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    Electronic Resource

    Σύνδεσμος: http://elar.uspu.ru/handle/uspu/13509
    Филологический класс. 2020. Т. 25, № 1