Modelado y difusión de temas noticiosos en medios socialescaracterísticas y factores de la emergencia de noticias en un canal informativo de Twitter

Referencias bibliográficas

• Arcila-Calderón, C., Barbosa-Caro, E. & Cabezuelo-Lorenzo, F. (2016). Técnicas big data: análisis de textos a gran escala para la investigación científica y periodística. epi, El Profesional de la Información, 25(4), 623-631.
• Argüelles, I. & Muñoz, A. (2012). An insight into twitter: A corpus based contrastive study in English and Spanish. Revista de Lingüística y Lenguas Aplicadas, 7, 37-50.
• Arora, S., Ge, R., Halpern, Y., Mimno, D., Moitra, A., Sontag. D., Wu, Y. & Zhu, M. (2013). Practical algorithm for topic modeling with Provable Guarantees. 30th International Conference on Machine Learning (icml), 28(2), 280-288. Atlanta, Estados Unidos.
• Asfari, O., Hannachi, L., Bentayeb, F. & Boussaid, O. (2013). Ontological topic modeling to extract Twitter users’ topics of interest. 8th International Conference on Information Technology and Applications (icita) (pp. 141-146). Sydney, Australia.
• Blei, D. (2012). Topic models and digital humanities. Journal of Digital Humanities, 2(1), 8-11.
• Blei, D. & McAuliffe, J. (2007). Supervised topic models. Neural Information Processing Systems, 20, 1-8.
• Bogdanov, P., Busch, M., Moehlis, J., Singh, A. K. & Szymanski, B. K. (2013). The social media genome: Modeling individual topic-specific behavior in social media. ieee/acm International Conference on Advances in Social Networks Analysis and Mining asonam (pp. 236-242). Niagara Falls, Canadá.
• Caballero, U. (2001). Periódicos mexicanos en internet. Revista Universidad de Guadalajara, 22(46).
• Cai, K., Spangler, S., Chen, Y. & Zhang, L. (2010). Leveraging sentimento analysis for topic detection. Web Intelligence and Agent Systems: An International Journal, 8, 291-302.
• Deutschmann, P. & Danielson, W. (1960). Diffusion of knowledge of the major news story. Journalism Quarterly, 37(3), 345-355.
• Emery, S., Szczypka, G., Abril, E., Kim, Y. & Vera, L. (2014). Are you scared yet? Evaluating fear appeal messages in tweets about the tips campaign. Journal of Communication, 64(2), 278-295.
• Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the acm, 56(4), 82-89. doi: https://doi.org/10.1145/2436256.2436274
• Ferrari, L., Rosi, A., Mamei, M. & Zambonelli, F. (2011). Extracting urban patterns from location-based social networks. Proceedings of the 3rd acm sigspatial international workshop on location-based social network (pp. 9-16). Chicago: acm.
• García de Torres, E., Rodrigues, J., Saiz, J., Albacar, H., Ruiz, S. & Martínez, S. (2008). Las herramientas 2.0 en los diarios españoles 2006-2008: tendencias. prisma.com, 7, 193-222.
• Gerber, M. (2014). Predicting crime using Twitter and Kernel Density estimation. Decision Support Systems, 61, 115-125.
• Ghosh, D. & Guha, R. (2013). What are we ‘tweeting’ about obesity? Mapping tweets with topic modeling and geographic information system. Cartography and Geographic Information Science, 40(2), 90-102.
• Greenberg, B. (1964). Diffusion of news of the Kennedy assassination. Public Opinion Quarterly, 28(2), 225-232.
• Henningham, J. (2000). The death of Diana: An Australian news diffusion study. Australian Journalism Review, 22(2), 23-33.
• Ibrahim, A., Ye, J. & Hoffner, C. (2008). Diffusion of news of the shuttle Columbia disaster: The role of emotional responses and motives for interpersonal communication. Communication Research Reports, 25(2), 91-101.
• Jungherr, A. (2014). The logic of political coverage on Twitter: Temporal dynamics and content. Journal of Communication, 64(2), 239- 259.
• Kang, B., O’Donovan, J. & Höllerer, T. (2012). Modeling topic specific credibility in Twitter. iui ‘12 Proceedings of the 2012 acm International Conference on Intelligent User Interfaces (pp. 179-188). Lisboa, Portugal.
• Kechaou, Z., Ammar, M. & Alimi, A. (2013). A multi-agent based system for sentiment analysis of user-generated content. International Journal on Artificial Intelligence Tools, 22(2), 1-28.
• Kim, D. & Oh, A. (2011). Topic chains for understanding a news corpus. cicling’11 Proceedings of the 12th International Conference on Computational Linguistics and Intelligent Text Processing-Volume Part II (pp. 163-176). Tokio, Japón.
• Lasorsa, D., Lewis, S. & Holton, A. (2012). Normalizing Twitter: Journalism practice in an emerging communication space. Journalism Studies, 13(1), 19-36.
• Leetaru, K. (2012). Data mining methods for the content analyst. An introduction to the computational analysis of content. Nueva York: Routledge.
• Lotan, G., Graeff, E., Ananny, M., Gaffney, D., Pearce, I. & Boyd, D. (2011). The revolutions were Tweeted: Information flows during the 2011 Tunisian and Egyptian revolutions. International Journal of Communication, 5, 1375-1405.
• Meena, A. & Prabhakar, T. (2007). Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis. En A. Amati, C. Carpineto & G. Romano (Eds.), Advances in Information Retrieval. ecir 2007. Lecture Notes in Computer Science, (vol. 4425). Berlín, Alemania: Springer.
• Michelson, M. & Macskassy, S. (2010). Discovering users’ topics of interest on Twitter: A first look. and’10 Proceedings of the fourth workshop on analytics for noisy unstructured text data (pp. 73-80). Toronto, Canadá.
• Micó, J. L., Canavilhas, J., Masip, P. & Ruiz, C. (2008). La ética en el ejercicio del periodismo: credibilidad y autorregulación en la era del periodismo en Internet. Estudos em Comunicação, 4, 15-39.
• Mukherjee, S. & Shaw, R. (2016). Big data. Concepts, applications, challenges and future scope. International Journal of Advanced Research in Computer and Communication Engineering, 5(2), 66-74.
• Newman, D., Chemudugunta, C., Smyth, P. & Steyvers, M. (2006). Analyzing entities and topics in news articles using statistical topic models. isi’06 Proceedings of the 4th ieee International Conference on Intelligence and Security Informatics (pp. 93-104). San Diego, Estados Unidos.
• Newman, N., Dutton, W. & Blank, G. (2012). Social media in the changing ecology of news: The fourth and fifth estates in Britain. International Journal of Internet Science, 7(1), 6-22.
• Paul, M. & Dredze, M. (2014). Discovering health topics in social media using topic models. PLoS one, 9(8), e103408.
• Peslak, A., Ceccucci, W. & Sendall, P. (2010). An empirical study of social networking behavior using diffusion of innovation theory. Conference on Information Systems Applied Research 2010 conisar Proceedings. Nashville, Estados Unidos.
• Rogers, E. (2000). Reflections on news event diffusion research. Journalism & Mass Communication Quarterly, 77(3), 561-576.
• Rogers, E. (2003). Diffusion of innovations. Nueva York: Free Press.
• Rogers, E. & Seidel, N. (2002). Diffusion of news of the terrorist attacks of september 11, 2001. Prometheus, 20(3), 209-219.
• Said, E., Serrano, A., García, E., Calderín, M., Rost, A., Arcila, C., Yezers’ka, L., Edo, C., Rojano, M., Jerónimo, P. & Sánchez, J. (2013). Ibero-American online news managers’ goals and handicaps in managing social media. Television and New Media, 4(2).
• Schultz, B. & Sheffer, M. L. (2012). New brand: The rise of the independent reporter through social media. Online Journal of Communication and Media Technologies, 2(3), 93-112.
• Stieglitz, S. & Dang-Xuan, L. (2013). Emotions and information diffusion in social media-Sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217-247.
• Stubbs, M. (2001). Words and phrases: Corpus studies of lexical semantics. Blackwell: Oxford.
• Turney, P. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (acl), (pp. 417-424) Philadelphia, Estados Unidos.
• Ularu, E., Puican, F., Apostu, A. & Velicanu, M. (2012). Perspectives on big data and big data analytics. Database Systems Journal, 3(4), 3-14.
• Ure, M. & Parselis, M. (2013). Argentine media and journalists enhancing and polluting of communication on Twitter. International Journal of Communication, 7, 1784-1800.
• Vinodhini, G. & Chandrasekaran, R. M. (2012). Sentiment analysis and opinion mining: A survey. International Journal of Advanced Research in Computer Science and Software Engineering, 2(6), 282- 292.
• Wasike, B. (2013). Framing news in 140 characters: How social media editors frame the news and interact with audiences via Twitter. Global Media Journal-Canadian Edition, 6(1), 5-23.
• Zhao, W., Jiang, J., Weng, J., He, J., Lim, E., Yan, H. & Li, X. (2011, 18-21 de abril). Comparing Twitter and traditional media using topic models. Advances in Information Retrieval: 33rd European Conference on ir Research ecir, 2011. Dublín, Irlanda.