Identificação da Reputação de Áreas Urbanas Externas com Dados de Mídias Sociais
Resumo
Aprender a percepção das pessoas que emerge dasáreas urbanas tem sido um objetivo de pesquisa multidisciplinar, pois oferece um grande potencial para facilitar a difícil tarefa de compreender as características intrínsecas dasáreas urbanas, por exemplo, sua reputação. Para isso, comumente, são exploradas abordagens tradicionais de coleta de dados, como entrevistas. No entanto, tais métodos não escalam facilmente, dificultando a execução desse tipo de análise para um grande número de lugares. Para superar esse desafio, propomos um método alternativo que explora dados de redes sociais baseadas em localização (LBSNs). O nosso método inovador, chamado de REP-Map, trata da descoberta e mapeamento da reputação dasáreas urbanas externas, explorando aspectos semânticos e espaciais em mensagens compartilhadas em LBSNs. Estudando áreas externas de Chicago, mostramos, através de uma pesquisa com voluntários, que nosso método pode capturar a reputação que os usuários consideram em relação a essasmáreas urbanas externas.
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Cranshaw, J., Schwartz, R., Hong, J. I., and Sadeh, N. (2012). The livehoods project: Utilizing social media to understand the dynamics of a city.
Ester, M., Kriegel, H.-P., Sander, J., Xu, X., et al. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, volume 96, pages 226–231.
Flaes, J. B., Rudinac, S., and Worring, M. (2016). What multimedia sentiment analysis says about city liveability. In European Conference on Information Retrieval, pages 824–829. Springer.
Gonçalves, P., Araújo, M., Benevenuto, F., and Cha, M. (2013). Comparing and combining sentiment analysis methods. In Proceedings of the rst ACM conference on Online social networks, pages 27–38. ACM.
Henshaw, V. (2013). Urban smellscapes: Understanding and designing city smell environments. Routledge.
Jiang, S., Qian, X., Mei, T., and Fu, Y. (2016). Personalized travel sequence recommendation on multi-source big social media. IEEE Transactions on Big Data, 2(1):43–56.
Kim, J., Cha, M., and Sandholm, T. (2014). Socroutes: safe routes based on tweet sentiments. In Proceedings of the 23rd International Conference on World Wide Web, pages 179–182. ACM.
Marsden, P. V. and Lin, N. (1982). Social structure and network analysis, volume 57. Sage.
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26, pages 3111–3119. Curran Associates, Inc.
Naik, N., Philipoom, J., Raskar, R., and Hidalgo, C. (2014). Streetscore-predicting the perceived safety of one million streetscapes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 779–785.
Quercia, D., Ellis, J., Capra, L., and Crowcroft, J. (2012). Tracking gross community happiness from tweets. In Proceedings of the ACM 2012 conference on computer supported cooperative work, pages 965–968. ACM.
Quercia, D., O’Hare, N. K., and Cramer, H. (2014a). Aesthetic capital: what makes london look beautiful, quiet, and happy? In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing, pages 945–955. ACM.
Quercia, D., Schifanella, R., and Aiello, L. M. (2014b). The shortest path to happiness: Recommending beautiful, quiet, and happy routes in the city. In Proceedings of the 25th ACM conference on Hypertext and social media, pages 116–125. ACM.
Quercia, D., Schifanella, R., Aiello, L. M., and McLean, K. (2015). Smelly maps: the digital life of urban smellscapes. arXiv preprint arXiv:1505.06851.
Silva, T. H., Vaz de Melo, P. O. S., Almeida, J. M., and Loureiro, A. A. F. (2013). Uma Fotograa do Instagram: Caracterização e Aplicação. In Proc. of XXXII SBRC, Brasília, DF.
Steiger, E., Resch, B., and Zipf, A. (2016). Exploration of spatiotemporal and semantic clusters International Journal of Geographical of twitter data using unsupervised neural networks. Information Science, 30(9):1694–1716.
Tasse, D., Liu, Z., Sciuto, A., and Hong, J. I. (2017). State of the geotags: Motivations and recent changes. In ICWSM, pages 250–259.
Weigelt, K. and Camerer, C. (1988). Reputation and corporate strategy: A review of recent theory and applications. Strategic management journal, 9(5):443–454.
Yang, D., Zhang, D., Yu, Z., and Wang, Z. (2013). A sentiment-enhanced personalized location recommendation system. In Proceedings of the 24th ACM Conference on Hypertext and Social Media, pages 119–128. ACM.
Yuan, N. J., Zheng, Y., Xie, X., Wang, Y., Zheng, K., and Xiong, H. (2015). Discovering urban functional zones using latent activity trajectories. IEEE Transactions on Knowledge and Data Engineering, 27(3):712–725.
Publicado
10/05/2018
Como Citar
SANTOS, Frances A.; SILVA, Thiago H.; LOUREIRO, Antonio A. F.; BOUKERCHE, Azzedine; VILLAS, Leandro A..
Identificação da Reputação de Áreas Urbanas Externas com Dados de Mídias Sociais. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 36. , 2018, Campos do Jordão.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 810-823.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2018.2460.
