Preferências Brasileiras de Leitura no Goodreads: Análises entre Estados e Regiões
DOI:
https://doi.org/10.5753/isys.2022.2411Keywords:
Livros, Goodreads, Perfis de Leitura, Identidade Cultural, Cultura Brasileira, Redes Multipartidas, Análise de Redes SociaisAbstract
Como uma nação multicultural e etnicamente diversa, o Brasil possui identidades culturais singulares em sotaques, gastronomia e tradições, também refletidas em sua literatura. Aqui, modelamos uma rede multipartida para realizar análises de comparação entre estados com base nas preferências de leitura brasileiras no Goodreads. Exploramos o impacto de fatores geográficos, socioeconômicos e demográficos em livros compartilhados e gêneros literários em todos os estados brasileiros. Essas análises entre estados destacam a rica diversidade cultural do país, onde cada região mostra sua própria identidade. Nossas descobertas abrem oportunidades para a indústria literária, aprimorando o conhecimento atual sobre indicadores sociais relacionados às preferências de leitura.
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Barbon Jr., S., Tavares, G. M., and Kido, G. S. (2017). Artificial and natural topic detection in online social networks. iSys - Brazilian Journal of Information Systems, 10(1):80–98.
Belinkov, Y. and Glass, J. R. (2019). Analysis methods in neural language processing: A survey. Trans. Assoc. Comput. Linguistics, 7:49–72.
Blondel, V. D. et al. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008.
Borges de Souza, T. R. C. et al. (2015). Brazilian cultural differences and their effects on the web interfaces user experience. In Int’l Conf Cross-Cultural Design Methods, Practice and Impact, pages 209–220.
Butts, C. T. (2020). sna: Tools for Social Network Analysis. R package version 2.6.
Cagliero, L. and Quatra, M. L. (2021). Inferring multilingual domain-specific word embeddings from large document corpora. IEEE Access, 9:137309–137321.
Carosia, A. E. O., Coelho, G. P., and Silva, A. E. A. (2021). Investment strategies applied to the brazilian stock market: A methodology based on sentiment analysis with deep learning. Expert Syst. Appl., 184:115470.
Choi, J. H. et al. (2006). Comparing world city networks: a network analysis of internet backbone and air transport intercity linkages. Global Networks, 6(1):81–99.
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic press.
Digiampietri, L. et al. (2014). Análise da rede dos doutores que atuam em computac¸ ˜ao no brasil. In BraSNAM, pages 33–44.
Fredrickson, M. M. and Chen, Y. (2019). Permutation and randomization tests for network analysis. Social Networks, 59:171–183.
García-Pérez, G., Boguñá, M., Allard, A., and Serrano, M. Á . (2016). The hidden hyperbolic geometry of international trade: World trade atlas 1870–2013. Scientific Reports, 6(1):33441.
Garner, J. (2020). Experiencing time in prison: the influence of books, libraries and reading. J. Documentation, 76(5):1033–1050.
Guarasci, R., Silvestri, S., Pietro, G. D., Fujita, H., and Esposito, M. (2022). BERT syntactic transfer: A computational experiment on italian, french and english languages. Comput. Speech Lang., 71:101261.
Igawa, R. A., Almeida, A., Zarpelão, B., and Barbon Jr., S. (2015). Recognition on online social network by user’s writing style. iSys - Brazilian Journal of Information Systems, 8(3):64–85.
Krackardt, D. (1987). Qap partialling as a test of spuriousness. Social networks, 9(2):171–186.
Krótkiewicz, M., Jodlowiec, M., and Wojtkiewicz, K. (2016). Introduction to semantic knowledge base: Multilanguage support of linguistic module. In European Network Intelligence Conference, ENIC, pages 188–194.
Kruskal, W. H. and Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260):583–621.
Lance, G. N. and Williams, W. T. (1966). Computer Programs for Hierarchical Polythetic Classification (“Similarity Analyses”). The Computer Journal, 9(1):60–64.
Lance, G. N. and Williams, W. T. (1967). Mixed-data classificatory programs i - agglomerative systems. Australian Computer Journal, 1(1):15–20.
Liu, M. et al. (2018). The relation of culture, socio-economics, and friendship to music preferences: A large-scale, cross-country study. PloS one, 13(12):e0208186.
Ma, Q. (2021). Reading attitude among elementary school students in china: A comparison between regions of different economic development. In ICIMTECH, pages 55:1–55:4. ACM.
Maity, S. K. et al. (2017). Book reading behavior on goodreads can predict the amazon best sellers. In ASONAM, pages 451–454.
Morais, J. I., Abonizio, H. Q., Tavares, G. M., da Fonseca, A. A., and Barbon Jr, S. (2020). A multi-label classification system to distinguish among fake, satirical, objective and legitimate news in brazilian portuguese. iSys - Brazilian Journal of Information Systems, 13(4):126–149.
Müller, M. (2021). Reading habits of young people in the context of digital progress: An example of research of republic croatia. In ICEIT, pages 219–225. IEEE.
Nascimento, M. L. et al. (2018). Uma análise do fator cultural em tecnologias persuasivas: um estudo de caso da rede social facebook. In BraSNAM. SBC.
Oliveira, D. N. O. and Merschmann, L. H. C. (2021). Joint evaluation of preprocessing tasks with classifiers for sentiment analysis in brazilian portuguese language. Multim. Tools Appl., 80(10):15391–15412.
Oliveira, G. P., Santos, M., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2020). Detecting collaboration profiles in success-based music genre networks. In ISMIR, pages 726–732.
Otter, D. W., Medina, J. R., and Kalita, J. K. (2021). A survey of the usages of deep learning for natural language processing. IEEE Trans. Neural Networks Learn. Syst., 32(2):604–624.
Pessutto, L. R. C., Vargas, D. S., and Moreira, V. P. (2020). Multilingual aspect clustering for sentiment analysis. Knowl. Based Syst., 192:105339.
Sabri, N. et al. (2020). A cross-country study on cultural similarities based on book preferences. Soc. Netw. Anal. Min., 10(1):86.
Serrano, M. Á . et al. (2009). Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences, 106(16):6483–6488.
Shahsavari, S. et al. (2020). An automated pipeline for character and relationship extraction from readers literary book reviews on goodreads.com. In WebSci, pages 277–286.
Silva, M. O., Scofield, C., Oliveira, G. P., Seufitelli, D. B., and Moro, M. M. (2021a). BraCID: Brazilian Cultural Identity Information Through Reading Preferences. DOI: 10.5281/zenodo.4890048.
Silva, M. O., Scofield, C., Oliveira, G. P., Seufitelli, D. B., and Moro, M. M. (2021b). Exploring brazilian cultural identity through reading preferences. In Brazilian Workshop on Social Network Analysis and Mining, pages 115–126, Porto Alegre, RS, Brasil. SBC.
Simpson, W. (2001). Qap: The quadratic assignment procedure. Technical Report 1.2, North American Stata Users’ Group Meetings 2001.
Wang, K. et al. (2019). Exploring goodreads reviews for book impact assessment. J. Informetrics, 13(3):874–886.
Wilcoxon, F. (1945). Individual comparisons by ranking methods. biom. bull., 1, 80–83.
Yucesoy, B., Wang, X., Huang, J., and Barabási, A. (2018). Success in books: a big data approach to bestsellers. EPJ Data Sci., 7(1):7.
Zhao, Y. et al. (2021). Do cultural differences affect users’ e-learning adoption? A meta-analysis. Br. J. Educ. Technol., 52(1):20–41.
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