Estrategias para la aplicación del Social Media Mining en las redes sociales de Facebook e Instagram

dc.contributor.advisorDuque Méndez, Néstor Daríospa
dc.contributor.authorZuluaga Gómez, Juan Camilospa
dc.date.accessioned2020-06-03T22:16:50Zspa
dc.date.available2020-06-03T22:16:50Zspa
dc.date.issued2020spa
dc.description.abstractDado el gran uso de las redes sociales en la actualidad, es indudable la gran cantidad de datos que se genera cada día y por lo cual es necesario tener la capacidad de hacer un análisis detallado. Debido a la particularidad que presentan los datos del social media, es importante que se requieren nuevas técnicas que puedan manejar eficazmente este nuevo tipo de data. El estudio y desarrollo de estas nuevas técnicas, se conoce como el Social Media Mining (SMM). Este trabajo se orientó a la construcción de un modelo que aborde los desafíos y oportunidades del SMM en las redes sociales de Facebook e Instagram. (Texto tomado de la fuente)spa
dc.description.abstractGiven the great use of social networks today, there is no doubt the large amount of data that is generated every day and therefore it is necessary to have the ability to make a detailed analysis. Due to the particularity of social media data, it is important that new techniques are required that can effectively handle this new type of data. The study and development of these new techniques is known as the Social Media Mining (SMM). This work was aimed at building a model that addresses the challenges and opportunities of the SMM in social networks Facebook and Instagram.eng
dc.description.additionalTesis presentada como requisito parcial para optar al título de: Magister en Administración de Sistemas Informáticos. -- Línea de Investigación: Tecnologías de la Información y Comunicación.spa
dc.description.degreelevelMaestríaspa
dc.format.extent80spa
dc.format.mimetypeapplication/pdfspa
dc.identifier.citationZuluaga Gómez, J.C. (2020). Estrategias para la aplicación del Social Media Mining en las redes sociales de Facebook e Instagram (tesis de maestría). Universidad Nacional de Colombia, Manizales, Colombiaspa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/77610
dc.language.isospaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Manizalesspa
dc.publisher.programManizales - Administración - Maestría en Administración de Sistemas Informáticosspa
dc.relation.referencesAdedoyin-olowe, M., Gaber, M. M., & Stahl, F. (2014). A Survey of Data Mining Techniques for Social Network Analysis. International Journal of Research in Computer Engineering and Electronics, 3(6), 1–8. Retrieved from http://jdmdh.episciences.org/18/pdf%5Cnhttp://jdmdh.episciences.org/18/spa
dc.relation.referencesAgyapong, K. B., Hayfron-Acquah, D. J. ., & Asante., D. M. (2016). An Overview of Data Mining Models (Descriptive and Predictive). International Journal of Software & Hardware Research in Engineering.spa
dc.relation.referencesAllem, J. P., & Ferrara, E. (2016). The importance of debiasing social media data to better understand e-cigarette-related attitudes and behaviors. Journal of Medical Internet Research. https://doi.org/10.2196/jmir.6185spa
dc.relation.referencesAlsmadi, I., & Gan, K. H. (2019). Review of short-text classification. International Journal of Web Information Systems, 15(2), 155–182. https://doi.org/10.1108/IJWIS-12-2017-0083spa
dc.relation.referencesAly, E. S., & van der Haar, D. T. (2020). Slang-Based Text Sentiment Analysis in Instagram. Advances in Intelligent Systems and Computing. https://doi.org/10.1007/978-981-32-9343-4_25spa
dc.relation.referencesAminolroaya, Z., & Katanforoush, A. (2017). How Iranian Instagram users act for parliament election campaign A study based on followee network. 2017 3rd International Conference on Web Research, ICWR 2017. https://doi.org/10.1109/ICWR.2017.7959297spa
dc.relation.referencesAnjaria, M., & Guddeti, R. M. R. (2014). A novel sentiment analysis of social networks using supervised learning. Social Network Analysis and Mining, 4(1), 1–15. https://doi.org/10.1007/s13278-014-0181-9spa
dc.relation.referencesArdehaly, E. M., & Culotta, A. (2018). Learning from noisy label proportions for classifying online social data. Social Network Analysis and Mining. https://doi.org/10.1007/s13278-017-0478-6spa
dc.relation.referencesBaj-Rogowska, A. (2017). Sentiment analysis of Facebook posts: The Uber case. 2017 IEEE 8th International Conference on Intelligent Computing and Information Systems, ICICIS 2017. https://doi.org/10.1109/INTELCIS.2017.8260068spa
dc.relation.referencesBakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. WWW’12 - Proceedings of the 21st Annual Conference on World Wide Web. https://doi.org/10.1145/2187836.2187907spa
dc.relation.referencesBampo, M., Ewing, M. T., Mather, D. R., Stewart, D., & Wallace, M. (2008). The effects of the social structure of digital networks on viral marketing performance. Information Systems Research, 19(3), 273–290. https://doi.org/10.1287/isre.1070.0152spa
dc.relation.referencesBarbier, G., & Liu, H. (2011). Social Network Data Analytics. Social Network Data Analytics. https://doi.org/10.1007/978-1-4419-8462-3spa
dc.relation.referencesCameron, J. J., Leung, C. K. S., & Tanbeer, S. K. (2011). Finding strong groups of friends among friends in social networks. Proceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011. https://doi.org/10.1109/DASC.2011.141spa
dc.relation.referencesChandrasekaran, S., Annamalai, B., & De, S. K. (2019). Evaluating marketer generated content popularity on brand fan pages – A multilevel modelling approach. Telematics and Informatics, 44(August), 101266. https://doi.org/10.1016/j.tele.2019.101266spa
dc.relation.referencesChen, S., Chen, S., Wang, Z., Liang, J., Yuan, X., Cao, N., & Wu, Y. (2017). D-Map: Visual analysis of ego-centric information diffusion patterns in social media. 2016 IEEE Conference on Visual Analytics Science and Technology, VAST 2016 - Proceedings. https://doi.org/10.1109/VAST.2016.7883510spa
dc.relation.referencesCulotta, A. (2014). Reducing Sampling Bias in Social Media Data for County Health Inference. Joint Statistical Meetings Proceedings. Retrieved from http://cs.iit.edu/~culotta/pubs/culotta14reducing.pdf%5Cnhttp://tapilab.github.io/public health/2014/08/02/bias/spa
dc.relation.referencesDasgupta, S. S., Natarajan, S., Kaipa, K. K., Bhattacherjee, S. K., & Viswanathan, A. (2015). Sentiment analysis of Facebook data using Hadoop based open source technologies. Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2015, (1), 3–5. https://doi.org/10.1109/DSAA.2015.7344883spa
dc.relation.referencesFan, R., Yu, Z., Guo, B., Wang, L., & Yang, D. (2017). Target Distribution Guided Network Sampling. Proceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017. https://doi.org/10.1109/CBD.2017.71spa
dc.relation.referencesFresno García, M., Daly, A., & Supovitz, J. (2015). Desvelando climas de opinión por medio del Social Media Mining y Análisis de Redes Sociales en Twitter: el caso de los Common Core State Standards. Redes: Revista Hispana Para El Análisis de Redes Sociales, 26(1), 3.spa
dc.relation.referencesGautam, G., & Yadav, D. (2014). Sentiment analysis of twitter data using machine learning approaches and semantic analysis. 2014 7th International Conference on Contemporary Computing, IC3 2014. https://doi.org/10.1109/IC3.2014.6897213spa
dc.relation.referencesGómez Rodríguez, M., Leskovec, J., & Schölkopf, B. (2013). Structure and dynamics of information pathways in online media. Proceedings of the Sixth ACM International Conference on Web Search and Data Mining - WSDM ’13, 23. https://doi.org/10.1145/2433396.2433402spa
dc.relation.referencesHernández, S., Sallis, P., & Garden, K. (2011). A signal denoising method for text meaning vectors. Proceedings - AMS 2011: Asia Modelling Symposium 2011 - 5th Asia International Conference on Mathematical Modelling and Computer Simulation. https://doi.org/10.1109/AMS.2011.16spa
dc.relation.referencesKaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59–68. https://doi.org/10.1016/j.bushor.2009.09.003spa
dc.relation.referencesKumaran, P., & Chitrakala, S. (2015). Information diffusion in online social network: Techniques, applications and challenges. 6th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2015. https://doi.org/10.1515/9783110450101-013spa
dc.relation.referencesLiu, H., Morstatter, F., Tang, J., & Zafarani, R. (2016). The good, the bad, and the ugly: uncovering novel research opportunities in social media mining. International Journal of Data Science and Analytics, 1(3), 1–7. https://doi.org/10.1007/s41060-016-0023-0spa
dc.relation.referencesMedhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011spa
dc.relation.referencesMislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P., & Rosenquist, J. N. (2011). Understanding the Demographics of Twitter Users. Int’l AAAI Conference on Weblogs and Social Media (ICWSM).spa
dc.relation.referencesMorstatter, F., Dani, H., Sampson, J., & Liu, H. (2016). Can One Tamper with the Sample API?: Toward Neutralizing Bias from Spam and Bot Content. WWW. https://doi.org/10.1145/2872518.2889372spa
dc.relation.referencesNoureen, R., Qamar, U., Khan, F. H., & Muhammad, I. (2018). InstaSent: A novel framework for sentiment analysis based on instagram selfies. Advances in Intelligent Systems and Computing. https://doi.org/10.1007/978-3-030-01054-6_23spa
dc.relation.referencesPăvăloaia, V.-D., Teodor, E.-M., Fotache, D., & Danileţ, M. (2019). Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences. Sustainability, 11(16), 4459. https://doi.org/10.3390/su11164459spa
dc.relation.referencesRamírez-Tinoco, F. J., Alor-Hernández, G., Sánchez-Cervantes, J. L., Olivares-Zepahua, B. A., & Rodríguez-Mazahua, L. (2018). A brief review on the use of sentiment analysis approaches in social networks. Advances in Intelligent Systems and Computing, 688, 263–273. https://doi.org/10.1007/978-3-319-69341-5_24spa
dc.relation.referencesZafarani, R., Abbasi, M. A., & Liu, H. (2014). Social media mining: An introduction. In Social Media Mining: An Introduction. https://doi.org/10.1017/CBO9781139088510spa
dc.relation.referencesYang, D., Chow, T. W. S., Zhong, L., Tian, Z., Zhang, Q., & Chen, G. (2018). True and fake information spreading over the Facebook. Physica A: Statistical Mechanics and Its Applications. https://doi.org/10.1016/j.physa.2018.04.026spa
dc.relation.referencesWani, M. A., Agarwal, N., Jabin, S., & Hussain, S. Z. (2019). Analyzing Real and Fake users in Facebook Network based on Emotions. 2019 11th International Conference on Communication Systems and Networks, COMSNETS 2019. https://doi.org/10.1109/COMSNETS.2019.8711124spa
dc.relation.referencesVaghela, V. B., & Jadav, B. M. (2016). Analysis of Various Sentiment Classification Techniques. International Journal of Computer Applications, 140(3), 22–27. https://doi.org/10.5120/ijca2016909259spa
dc.relation.referencesVinodhini, G., & Chandrasekaran, R. (2012). Sentiment Analysis and Opinion Mining : A Survey International Journal of Advanced Research in Sentiment Analysis and Opinion Mining : A Survey. International Journal of Advanced Research in Computer Science and Software Engineering, 2(6), 283–292.spa
dc.rightsDerechos reservados - Universidad Nacional de Colombiaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.spaAcceso abiertospa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.ddc000 - Ciencias de la computación, información y obras generalesspa
dc.subject.ddc300 - Ciencias sociales::302 - Interacción socialspa
dc.subject.proposalSocial Media Miningeng
dc.subject.proposalSocial Media Miningspa
dc.subject.proposalSocial networkseng
dc.subject.proposalRedes socialesspa
dc.subject.proposalAnálisis de sentimientosspa
dc.subject.proposalSentiment analysiseng
dc.subject.proposalInformation disseminationeng
dc.subject.proposalDifusión de la informaciónspa
dc.subject.proposalSampling biaseng
dc.subject.proposalSesgo de muestreospa
dc.subject.proposalNoise eliminationeng
dc.subject.proposalEliminación del ruidospa
dc.subject.proposalFacebookspa
dc.subject.proposalFacebookeng
dc.subject.proposalInstagrameng
dc.subject.proposalInstagramspa
dc.titleEstrategias para la aplicación del Social Media Mining en las redes sociales de Facebook e Instagramspa
dc.title.alternativeStrategies for the application of Social Media Mining in the social networks of Facebook and Instagramspa
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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