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

Palabras clave: Twitter, Difusión de noticias, Medios sociales, Modelamiento de temas, Big Data

Resumen

Este estudio busca caracterizar el modelado y difusión de temas noticiosos en medios sociales y determinar los factores que influyan en su aparición. Con técnicas en torno a la filosofía del Big Data se analizó un año de tuits del medio colombiano El Tiempo, encontrando que la aparición de temas en el largo plazo se relaciona con atributos del mensaje. Se mencionan implicaciones teóricas y contribuciones para otros modelos a la luz del modelo de Difusión de Innovaciones.

Citas

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.

Publicado
2019-03-06
Cómo citar
Arcila Calderón, C., Barbosa Caro, E., & Aguaded, I. (2019). Modelado y difusión de temas noticiosos en medios sociales: características y factores de la emergencia de noticias en un canal informativo de Twitter. Comunicación Y Sociedad, 1-21. https://doi.org/10.32870/cys.v2019i0.6437
Sección
Temática general