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1Academic Journal
Συγγραφείς: Nerini, Amanda, Cardelli, Anna, Matera, Camilla
Πηγή: Body Image. 54:101927
Θεματικοί όροι: Verbal disclaimer, Visual disclaimer, Positive body image, Social networks users, Young women
Συνδεδεμένο Πλήρες ΚείμενοΠεριγραφή αρχείου: application/pdf
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2Academic Journal
Συγγραφείς: Saber Ariamanesh, Farigh Mirogheshlagh, Marzieh Nouri Fard
Πηγή: Taḥqīqāt-i ̒Ulūm-i Raftārī, Vol 20, Iss 4, Pp 577-586 (2023)
Θεματικοί όροι: Psychiatry, Neurophysiology and neuropsychology, QP351-495, RC435-571, social networks users, sleep quality, covid - 19, 3. Good health
Σύνδεσμος πρόσβασης: https://doaj.org/article/7b93736d8af44d4289c3344bd1a77d0e
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3Academic 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
Σύνδεσμος πρόσβασης: https://doaj.org/article/f60f5a9a613d41a5bb8accc67669c4f5
https://openrepository.ru/article?id=461345
https://cyberleninka.ru/article/n/tipologiya-polzovateley-sotsialnyh-setey-v-metaforicheskom-zerkale-rossiyskih-mass-media
https://filclass.ru/archive/2020/tom-25-1/tipologiya-polzovatelej-sotsialnykh-setej-v-metaforicheskom-zerkale-rossijskikh-mass-media
https://www.openrepository.ru/article?id=461345
http://elar.uspu.ru/bitstream/uspu/13509/1/fkls-2020-01-06.pdf
https://filclass.ru/images/JOURNAL/2020-1-25/6.pdf
http://elar.uspu.ru/handle/uspu/13509
https://elar.uspu.ru/handle/ru-uspu/54642 -
4Academic Journal
Συγγραφείς: TANYERİ, Emel, TOPRAK, Hande
Πηγή: Volume: 16, Issue: 31 4265-4288
OPUS Uluslararası Toplum Araştırmaları Dergisi
OPUS International Journal of Society ResearchesΘεματικοί όροι: Nüfuzlu, Nüfuz Pazarlaması, Satın Alma Davranışı, Sosyal Ağ Kullanıcıları, 0508 media and communications, Sociology, 0502 economics and business, 05 social sciences, Influential, Influencer Marketing, Purchase Behavior, Social Networks Users, Sosyoloji
Περιγραφή αρχείου: application/pdf
Σύνδεσμος πρόσβασης: https://dergipark.org.tr/tr/download/article-file/1035511
https://app.trdizin.gov.tr/makale/TkRBek5ERXlNZz09/nufuz-pazarlamasi-influencer-marketing-ve-satin-alma-davranisi-iliskisi-sosyal-ag-kullanicilari-uzerinden-bir-arastirma
https://dergipark.org.tr/en/download/article-file/1035511
https://dergipark.org.tr/tr/pub/opus/issue/57675/714203
https://avesis.erciyes.edu.tr/publication/details/12d61959-0fba-4322-b6c0-299bf6335977/oai
https://dergipark.org.tr/tr/pub/opus/issue/57675/714203 -
5Academic Journal
Συγγραφείς: Yosra Mouelhi, Marine Alessandrini, Vanessa Pauly, Bertrand Dussol, Stéphanie Gentile
Πηγή: BMC Nephrology, Vol 18, Iss 1, Pp 1-8 (2017)
Θεματικοί όροι: Characteristics, Inclusion, Internet, Profiles, Renal transplant recipient, Social networks users, Diseases of the genitourinary system. Urology, RC870-923
Περιγραφή αρχείου: electronic resource
Relation: http://link.springer.com/article/10.1186/s12882-017-0670-y; https://doaj.org/toc/1471-2369
Σύνδεσμος πρόσβασης: https://doaj.org/article/40d029e984984dbd81ef46a1d46102f6
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6eBook
Συνεισφορές: Alhajj, Reda, editorAff1, Rokne, Jon, editorAff2
Πηγή: Encyclopedia of Social Network Analysis and Mining. :2760-2760
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7eBook
Συνεισφορές: Alhajj, Reda, editorAff1, Rokne, Jon, editorAff2
Πηγή: Encyclopedia of Social Network Analysis and Mining. :1918-1918
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8Dissertation/ 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
Θεματικοί όροι: Markov Chain Monte Carlo, Networks, Bayesian statistics, Latent space model, Online social networks -- Users -- Statistics, Latent variables, Statistical models, Redes sociales en línea, Variables latentes, Modelos estadísticos, Cadena de Markov de Monte Carlo, Estadística bayesiana, Redes sociales, Modelo de espacio latente
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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9Dissertation/ Thesis
Συγγραφείς: Rivera Agredo, Ferney Mauricio
Συνεισφορές: Mahecha Sánchez, Gloria Andrea
Θεματικοί όροι: Free expression -- Adolescents, Human dignity, Social media, Social networks -- Users, Libre expresión -- Adolecentes, Dignidad humana, Redes sociales, Redes sociales -- Usuarios
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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10Academic Journal
Συγγραφείς: Ledovaya, Yanina A., Tikhonov, Roman V., Ivanov, Viktor Yu., Yaminov, Bulat R., Bogolyubova, Olga N.
Θεματικοί όροι: methods of data collection, the milestones of an internet based study, social networks, Facebook, social networks users’ behavior, data collection by means of application, the feedback in internet based studies
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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11Dissertation/ Thesis
Συγγραφείς: Průchová, Markéta
Συνεισφορές: Luštický, Martin, Musil, Martin
Θεματικοί όροι: cestovní ruch, marketing, sociální sítě, Toulava, uživatelé sociálních sítí, social networks, social networks users, tourism
Περιγραφή αρχείου: application/pdf
Relation: https://vskp.vse.cz/eid/76495
Διαθεσιμότητα: https://vskp.vse.cz/eid/76495
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12Electronic Resource
Συγγραφείς: Кондратьева, О. Н., Kondratyevа, O. N.
Όροι ευρετηρίου: ЯЗЫКОЗНАНИЕ, ЛИНГВИСТИКА ТЕКСТА, РОССИЯ, РУССКИЙ ЯЗЫК, ИНТЕРНЕТ, ИНТЕРНЕТ-КОММУНИКАЦИИ, ИНТЕРНЕТ-ДИСКУРС, ИНТЕРНЕТ-ТЕКСТЫ, СОЦИАЛЬНЫЕ СЕТИ, МЕДИАЛИНГВИСТИКА, МЕДИАДИСКУРС, МЕДИАТЕКСТЫ, ИНТЕРНЕТ-ПОЛЬЗОВАТЕЛИ, ПОЛЬЗОВАТЕЛИ СОЦИАЛЬНЫХ СЕТЕЙ, КОНЦЕПТУАЛЬНЫЕ МЕТАФОРЫ, МЕТАФОРИЧЕСКИЕ МОДЕЛИ, МЕТАФОРИЧЕСКОЕ МОДЕЛИРОВАНИЕ, ЯЗЫКОВЫЕ СРЕДСТВА, МЕТАФОРИЧЕСКИЕ НОМИНАЦИИ, МЕТАФОРИЧЕСКАЯ ЭКСПАНСИЯ, КОНТЕНТ-АНАЛИЗ, ТИПЫ ПОЛЬЗОВАТЕЛЕЙ, ПОВЕДЕНЧЕСКИЕ ХАРАКТЕРИСТИКИ, ТИПЫ ПОВЕДЕНИЯ, ИНТЕРНЕТ-ПОВЕДЕНИЕ, ТИПОЛОГИИ ПОЛЬЗОВАТЕЛЕЙ, ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ, СМИ, СРЕДСТВА МАССОВОЙ ИНФОРМАЦИИ, РОССИЙСКИЕ СМИ, CONCEPTUAL METAPHORS, METAPHORICAL MODELS, METAPHORICAL MODELING, MEDIA LINGUISTICS, MEDIA DISCOURSE, MEDIA TEXTS, SOCIAL NETWORKS, INTERNET DISCOURSE, SOCIAL NETWORKS USERS, RUSSIAN LANGUAGE, Article
Σύνδεσμος:
http://elar.uspu.ru/handle/uspu/13509
Филологический класс. 2020. Т. 25, № 1