Análise da Popularidade, Visibilidade e Atividade de Diferentes Tipos de Robôs na Rede Social Twitter
Resumo
O Twitter é uma das maiores redes sociais da atualidade. Ela é utilizada por milhões de pessoas. Além de pessoas, esta rede social também é populada por robôs, que são programas de computador que agem na rede social realizando as mesmas ações que seres humanos realizam. Neste trabalho apresenta-se uma análise de 27 robôs no Twitter com o objetivo de investigar características dos conteúdos publicados, atuação e atenção recebida por eles. Três métricas são consideradas: visibilidade, atividade e popularidade. Os robôs são analisados de forma individual e por tipo de robô, que são: informativos, inteligentes interativos e inteligentes não-interativos. Os resultados mostram que os robôs diferem muito entre eles em termos do nível de atividade. Eles também têm visibilidade e popularidade diferentes. Análises de correlação revelam que popularidade e visibilidade são características que estão fortemente correlacionadas. Robôs inteligentes interativos exibem maior nível atividade. Maior visibilidade e popularidade são obtidas por robôs informativos. Diversas implicações desses resultados são apresentadas e discutidas.
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Araújo, M., Gonçalves, P., Benevenuto, F., and Cha, M. (2013). Métodos para análise de sentimentos no twitter. In Proceedings of the 19th Brazilian symposium on Multimedia and the Web (WebMedia’13).
Boden, M. A. (2009). Computer models of creativity. AI Magazine, 30(3):23.
Boshmaf, Y., Muslukhov, I., Beznosov, K., and Ripeanu, M. (2011). The socialbot network: when bots socialize for fame and money. In Proceedings of the 27th annual computer security applications conference, pages 93–102. ACM.
Bruns, A. and Stieglitz, S. (2013). Towards more systematic Twitter analysis: Metrics for tweeting activities. International Journal of Social Research Methodology, 16(2):91–108.
Cha, M., Haddadi, H., Benevenuto, F., and Gummadi, P. K. (2010). Measuring user influence in Twitter: The million follower fallacy. ICWSM, 10(10-17):30.
Chae, B. K. (2015). Insights from hashtag# supplychain and twitter analytics: Considering twitter and twitter data for supply chain practice and research. International Journal of Production Economics, 165:247–259.
Colton, S. (2008). Creativity versus the perception of creativity in computational systems. In AAAI Spring Symposium: Creative Intelligent Systems, pages 14–20.
Edwards, C., Edwards, A., Spence, P. R., and Shelton, A. K. (2014). Is that a bot running the social media feed? testing the differences in perceptions of communication quality for a human agent and a bot agent on twitter. Computers in Human Behavior, 33:372 – 376.
Ellison, N. B. et al. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1):210–230.
Ferrara, E. (2016). How twitter bots played a role in electing donald trump. http://www.wired.co.uk/article/twitter-bots-democracy-usa-election (Acesso: 28 de fevereiro de 2017).
Ferrara, E., Varol, O., Davis, C., Menczer, F., and Flammini, A. (2016). The rise of social bots. Commun. ACM, 59(7):96–104.
Figueiredo, F., Almeida, J. M., Benevenuto, F., and Gummadi, K. P. (2014). Does content determine information popularity in social media?: A case study of youtube videos’ content and their popularity. CHI ’14, pages 979–982, New York, NY, USA. ACM.
Furtado, A. and Andrade, N. (2011). Ativistas, passageiros, ocasionais e especialistas: Perfis de usuário na construção de um site de Q&A. In Anais do VIII Simpósio Brasileiro de Sistemas Colaborativos.
Hargittai, E. and Walejko, G. (2008). The participation divide: Content creation and sharing in the digital age. Information, Communication & Society, 11(2):239–256.
Haustein, S., Bowman, T. D., Holmberg, K., Tsou, A., Sugimoto, C. R., and Larivière, V. (2016). Tweets as impact indicators: Examining the implications of automated “bot” accounts on twitter. Journal of the Association for Information Science and Technology, 67(1):232–238.
Orcutt, M. (2012). Twitter mischief plagues mexico’s election. http://www.technologyreview.com/news/428286/twitter-mischief-plagues-mexicoselection/ (Acesso: 28 de fevereiro de 2017).
Ponciano, L. and Brasileiro, F. (2014). Finding volunteers’ engagement profiles in human computation for citizen science projects. Human Computation, 1(2):245–264.
Ponciano, L., Brasileiro, F., Simpson, R., and Smith, A. (2014). Volunteers’ engagement in human computation for astronomy projects. Computing in Science and Engineering, 16(6):52–59.
Twitter (2017a). Dados do uso do Twitter. https://about.twitter.com/pt/company (Acesso: 28 de fevereiro de 2017).
Twitter (2017b). Twitter developer documentation. https://dev.twitter.com/overview/documentation (Acesso: 28 de fevereiro de 2017).
van Mierlo, T. (2014). The 1% rule in four digital health social networks: An observational study. J Med Internet Res, 16(2):e33.
Varol, O., Ferrara, E., Clayton A. Davis, F. M., and Flammini, A. (2017). Online humanbot interactions: Detection, estimation, and characterization. In The International Conference on Weblogs and Social Media, pages 1–10.
Veale, T. (2015). Computational creativity tutorial. http://videolectures.net/iccc2014_veale_creativity_tutorial/ (Acesso: 28 de fevereiro de 2017).
Wald, R., Khoshgoftaar, T., Napolitano, A., and Sumner, C. (2013). Predicting susceptibility to social bots on twitter. In Information Reuse and Integration (IRI), 2013 IEEE 14th International Conference on, pages 6–13.
Publicado
02/07/2017
Como Citar
VALGAS, Andreza; CARMO, Felipe; PONCIANO, Lesandro; GÓES, Luís.
Análise da Popularidade, Visibilidade e Atividade de Diferentes Tipos de Robôs na Rede Social Twitter. In: SIMPÓSIO BRASILEIRO DE SISTEMAS COLABORATIVOS (SBSC), 14. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 1342-1356.
ISSN 2326-2842.
DOI: https://doi.org/10.5753/sbsc.2017.9958.
