Social big data, sociología y ciencias sociales computacionales

  1. Gualda, Estrella
Revista:
Empiria: Revista de metodología de ciencias sociales

ISSN: 1139-5737

Año de publicación: 2022

Título del ejemplar: El Big data en las ciencias sociales

Número: 53

Páginas: 147-177

Tipo: Artículo

DOI: 10.5944/EMPIRIA.53.2022.32631 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Empiria: Revista de metodología de ciencias sociales

Resumen

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Referencias bibliográficas

  • AHMED, B. (2015): “Lexical Normalisation of Twitter Data”. Arxiv. Computation and Language, https://arxiv.org/abs/1409.4614.
  • ALEXANDER, J.C. Y GIESEN, B. (1987): “From Reduction to Linkage: The Long View of theMicro – Macro Link”. The Micro-Macro Link. Berkeley y Los Angeles: University of California Press, pp.1-42.
  • ARCILA, C., BLANCO, D. & VALDEZ, B. (2020): “Rejection and Hate Speech in Twitter: Content Analysis of Tweets about Migrants and Refugees in Spanish”. Revista Española de Investigaciones Sociológicas, 172, pp.21-40.
  • ARCILA-CALDERÓN, C.; BLANCO-HERRERO, D.; FRÍAS-VÁZQUEZ, M. Y SEOANE-PÉREZ, F. (2021): “Refugees Welcome? Online Hate Speech and Sentiments in Twitter in Spain during the Reception of the Boat Aquarius”. Sustainability, 13(5), 2728. MDPI AG, http://dx.doi.org/10.3390/su13052728
  • AVAAZ (2019): Far Right Networks of Deception. Avaaz Report 22/5/2019, https://secure.avaaz.org/avaaz_report_network_deception_20190522.pdf.
  • BAPTISTE, K. (2020): “Mass personalization: Predictive marketing algorithms and the reshaping of consumer knowledge”.  Big Data & Society,  7(2), doi:http://dx.doi. org/10.1177/2053951720951581
  • BARTOSIK-PURGAT, M., Y RATAJCZAK-MROÅ»EK, M. (2018): Big data analysis as a source of companies' competitive advantage: A review. Entrepreneurial Business and Economics Review, vol. 6(4), 197, http://0-dx.doi.org.columbus.uhu.es/10. 15678/EBER.2018.060411
  • BELLMORE, A.; CALVIN, A.J.; XU, J.M. Y ZHU, X. (2015): “The five W’s of ‘bullying’ on Twitter: Who, What, Why, Where, and When”.  Computers in Human Behavior, 44, pp.305–314, https://doi.org/10.1016/j.chb.2014.11.052
  • BELLO-ORGAZ, G.; JUNG, J.J.; CAMACHO, D. (2016): “Social big data: Recent achievements and new challenges”. Information Fusion, 28, pp. 45-59, https://doi. org/10.1016/j.inffus.2015.08.005
  • BELLO-ORGAZ, G., HERNANDEZ-CASTRO, J., CAMACHO, D. (2017): “Detecting discussion communities on vaccination in Twitter”. Future Generation Computer Systems, 66, pp. 125-136, https://doi.org/10.1016/j.future.2016.06.032
  • BENOIT, K.; WATANABE, K.; WANG, H. et al. (2018): “quanteda: An R package for the quantitative analysis of textual data”. Journal of Open Source Software, 3(30), 774, https://joss.theoj.org/papers/10.21105/joss.00774
  • BEYER, M. Y LANEY, D. (2012): “The Importance of “Big Data”: A Definition”, https://www.gartner.com/en/documents/2057415/the-importance-of-big-data-a-definition
  • BRUGNOLI, E.; CINELLI, M.; QUATTROCIOCCHI, W. Y SCALA, A. (2019): “Recursive patterns in online echo chambers”.  Sci Rep  9,  20118, https://doi. org/10.1038/s41598-019-56191-7
  • BULGER, M.; TAYLOR, G. Y SCHROEDER, R. (2014): “Engaging Complexity: Challenges and Opportunities of Big Data”, London: NEMDOE.
  • BURGESS, J. Y BRUNS, A. (2012): “Twitter Archives and the Challenges of “Big Social Data” for Media and Communication Research”. M/C Journal, vol. 15, 5, http:// journal.media-culture.org.au/index.php/mcjournal/rt/printerFriendly/561Driscoll/0
  • BURNAP, P. Y WILLIAMS, M.L. (2016): “Us and them: Identifying cyber hate on Twitter across multiple protected characteristics”. EPJ Data Sci, 5, 11, https:// doi. org/10.1140/epjds/s13688-016-0072-6
  • CALDERÓN, C. A.; DE LA VEGA, G. Y HERRERO, D. B. (2020): “Topic Modeling and Characterization of Hate Speech against Immigrants on Twitter around the Emergence of a Far-Right Party in Spain”. Social Sciences, 9(11), 188. MDPI AG, http://dx.doi.org/10.3390/socsci9110188
  • CASAS, A.; DAVESA, F. Y CONGOSTO, M. (2016): “La cobertura mediática de una acción “conectiva”: la interacción entre el movimiento 15-M y los medios de comunicación”. Revista Española de Investigaciones Sociológicas, 155, pp.73-96, http:// dx.doi.org/10.5477/cis/reis.155.73
  • CHEN, Y., WU, X., HU, A. et al. (2021): “Social prediction: a new research paradigm based on machine learning”. The Journal of Chinese Sociology, 8, 15. https://doi. org/10.1186/s40711-021-00152-z
  • CINELLI, M. et al. (2020): “Echo Chambers on Social Media: A comparative analysis”. Physics and Society. arXiv:2004.09603 [physics.soc-ph] arXiv.org physics > arXiv:2004.09603
  • CONGOSTO MARTÍNEZ, M. (2016): Caracterización de usuarios y propagación de mensajes en Twitter en el entorno de temas sociales. Tesis Doctoral defendida en la Universidad Carlos III de Madrid. En http://hdl.handle.net/10016/22826.
  • CONGOSTO, M., BASANTA-VAL, P. Y SÁNCHEZ-FERNÁNDEZ, L. (2017): “THoarder: A framework to process Twitter data streams”. Journal of Network and Computer Applications, vol. 83, 1, 28-39.
  • DEL VECCHIO, P.; MELE, G.; NDOU, V. Y SECUNDO, G. (2018): “Creating value from Social Big Data: Implications for Smart Tourism Destinations”, Information Processing & Management, Volume 54, Issue 5, 2018, pp.847-860, https://doi. org/10.1016/j.ipm.2017.10.006.
  • DI FRANCO, G., SANTURRO, M. (2021): “Aprendizaje automático, redes neuronales artificiales e investigación social”. Quality & Quantity, 55, pp.1007–1025. https:// doi.org/10.1007/s11135-020-01037-y
  • EDELMANN, A.; WOLFF, T.; MONTAGNE, D.; BAIL, C. (2020): “Computational Social Science and Sociology”. Annual Review of Sociology, 46, https://doi. org/10.1146/annurev-soc-121919-054621
  • ELLIOTT, M. Y VALLIANT, R. (2017): “Inference for Nonprobability Samples. Statistical Science”. pp. 249-264, https://doi.org/10.1214/16-STS598
  • EVANS, J., Y FOSTER, J. G. (2019): “Computation and the Sociological Imagination”. Contexts, 18(4), pp. 10–15, https://doi.org/10.1177/1536504219883850
  • FERRARA, E. (2017): Disinformation and social bot operations in the run up to the 2017 French presidential election. First Monday. [S.l.], https:// doi.org/ 10.5210/ fm. v22i8.8005
  • FLORES, R.D. (2017): “Do Anti-Immigrant Laws Shape Public Sentiment? A Study of Arizona’s SB 1070 Using Twitter Data.” American Journal of Sociology, 123(2), pp.333–84
  • FRANKE, B. ET AL. (2016): Statistical Inference, Learning and Models in Big Data. International Statistical Review, 84, 3, pp.–389, https://doi.org/10.1111/insr.12176
  • GABDRAKHMANOVA, N.; PILGUN, M. (2021): “Intelligent Control Systems in Urban Planning Conflicts: Social Media Users’ Perception”. Applied Sciences, 11, 6579. https://doi.org/ 10.3390/app11146579
  • GALLEGO, M.; GUALDA, E. Y REBOLLO, C. (2017): “Women and Refugees in Twitter: Rethorics of Abuse, Vulnerability and Violence from a Gender Perspective”, Journal of Mediterranean Knowledge, 2(1), pp.37-58, http://www.mediterraneanknowledge.org/publications/index.php/journal/article/view/65
  • GUALDA, E. (2020): “Social network analysis, social big data and conspiracy theories”. Butter, M. & Knight, P. (Ed.): Handbook of Conspiracy Theories. London: Routledge, pp.135-147.
  • GUALDA, E. Y REBOLLO, C. (2020): “Big data y Twitter para el estudio de procesos migratorios: Métodos, técnicas de investigación y software”. Empiria. Revista de metodología en ciencias sociales, 46, pp.147-177, http://revistas.uned.es/index.php/ empiria/article/view/26970
  • GUALDA, E. (2016): Spanish General Elections, Microdiscourses Around #20D and Social Mobilisation on Twitter: Reality or Appearance?, Freire, F.C. et al. (eds.): Media and Metamedia Management: Switzerland: Springer International Publishing, pp. 67-77.
  • HASHEMA, I.A.T; YAQOOBA, I; ANUARA, N.B. et al. (2015): “The rise of big data on cloud computing: review and open research issues”, Information Systems, 47, pp.98–115.
  • HE, J. Y XIONG, N. (2018): “An effective information detection method for social big data. Multimedia Tools and Applications”, 77(9), pp.11277-11305, https://doi. org/10.1007/s11042-017-5523-y
  • JIANG, Y., DENG, S., LI, H., & LIU, Y. (2021). “Predicting user personality with social interactions in Weibo”. Aslib Journal of Information Management, 73(6), 839-864. http://dx.doi.org/10.1108/AJIM-02-2021-0048
  • JIN, X. ET AL. (2015): “Significance and Challenges of Big Data Research”. Big Data Research 2, pp.59–64.
  • KEIDING, N. Y LOUIS, T. (2016): “Perils and potentials of self-selected entry to epidemiological studies and surveys”. Journal of the Royal Statistical Society: Series A (Statistics in Society), 179, pp. 319-376, https://doi.org/10.1111/rssa.12136
  • KUMAR, A. Y JAISWAL, A. (2019): “Swarm intelligence based optimal feature selection for enhanced predictive sentiment accuracy on twitter”. Multimedia Tools and Applications, 1-25, http://0-dx.doi.org.columbus.uhu.es/10.1007/s11042-019-7278-0
  • LANEY, D. (2001, 6 de febrero): “3D Data Management: Controlling Data Volume, Velocity, and Variety”, Gartner, file No. 949, https://idoc.pub/documents/3d-datamanagement-controlling-data-volume-velocity-and-variety-546g5mg3ywn8
  • LIU, B. (2012): “Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies”, 5(1), pp.1-167, https://doi.org/10.2200/s00416ed1v01y201204hlt016
  • MORSTATTER, F.; PFEFFER, J.; LIU, H., & CARLEY, K. (2013): “Is the sample good enough? Comparing data from twitter’s streaming api with twitter’s firehose”. Proceedings of the International AAAI Conference on Web and Social Media, 7(1), https://arxiv.org/abs/1306.5204
  • MACAVANEY, S.; YAO, H.; YANG, E. et al. (2019): “Hate Speech detection: Challenge and solutions”. Plos One, 14(8), https://doi.org/10.1371/journal.pone.0221152
  • MARTÍ, M. A. (2003): “Introducción”. Tecnologías del lenguaje. Barcelona: Editorial UOC, pp.9-29.
  • MOHAMMAD, S.M.; KIRITCHENKO, S.; ZHU, X. Y MARTIN, J. (2015):  “Sentiment, Emotion, Purpose, and Style in Electoral Tweets”.  Information Processing and Management, 51(4), pp.480–499.
  • PLEBE, A. Y GRASSO, G. (2019): “The Unbearable Shallow Understanding of Deep Learning”. Minds & Machines 29, pp.515–553. https://doi.org/10.1007/s11023-019- 09512-8
  • RAHMAN, M.M.; ALI, G.G.M.N.; LI, X.J. et al. (2021): “Socioeconomic factors analysis for COVID-19 US reopening sentiment with Twitter and census data”. Heliyon, 7,2:e06200, https://doi.org/10.1016/j.heliyon.2021.e06200
  • MOLINA, M. Y GARIP, F. (2019): “Machine Learning for Sociology”. Annual Review of Sociology, 45, pp.27-45, https://doi.org/10.1146/annurev-soc-073117-041106
  • MÜNCH, R. Y SMELSER, N.J. (1987): “Relating the Micro and Macro”. En Alexander et al. (eds). “From Reduction to Linkage: The Long View of the Micro – Macro Link”. The Micro-Macro Link. Berkeley y Los Angeles: University of California Press, pp.356-387.
  • OLSHANNIKOVA, E., OLSSON, T., HUHTAMÄKI, J. et al. (2017): “Conceptualizing Big Social Data”. Journal of Big Data 4, 3, en https://doi.org/10.1186/s40537-017- 0063-x
  • PATGIRI, R. Y AHMED, A. (2016): “Big Data: The V’s of the Game Changer Paradigm”. 18th IEEE High Performance Computing and Communications, Sydney, https://doi.org/10.1109/HPCC-SmartCity-DSS.2016.0014
  • PICCIALLI, F. Y JUNG, J. E. (2017): “Understanding customer experience diffusion on social networking services by big data analytics”. Mobile Networks and Applications, 22(4), pp.605-612, en http://dx.doi.org/10.1007/s11036-016-0803-8
  • ROBLES, J.M.; TINGUARO, J.; CABALLERO, R. Y GÓMEZ, D. (2020): Big data para científicos sociales. Una introducción. Centro de Investigaciones Sociológicas.
  • SÁNCHEZ, P. Y ARCILA, C. (2020): “Supervised Sentiment Analysis of Science Topics: Developing a Training Set of Tweets in Spanish”. Journal of Information Technology Research, 13(3).
  • SHU, K.; SLIVA, A.; WANG, S., et al. (2017): “Fake News Detection on Social Media: A Data Mining Perspective”. ACM SIGKDD Explorations Newsletter archive, 19(1), pp.22-36.
  • SILGE, J. Y ROBINSON, D. (2021): Text Mining with R! O’Reilly. https://www.tidytextmining.com/
  • STOUT, C. T.; COULTER, K. Y EDWARDS, B. (2017): “#BlackRepresentation, Intersectionality, and Politicians’ Responses to Black Social Movements on Twitter. Mobilization: An International Quarterly”, 22(4), pp.493-509, https://doi. org/10.17813/1086-671X-22-4-493
  • VAJJALA, V.; MAJUMDER, B.; GUPTA, A. Y SURANA, H. (2020): Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems. Boston: O'Reilly.
  • WELBERS, K.; VAN ATTEVELDT, W. Y BENOIT, K. (2017): “Text analysis in R”. Communications Methods and Measures, 11(4), pp-245–265, https://doi.org/10.108 0/19312458.2017.1387238