Visibilidade no Facebook: Modelos, Medições e Implicações
Resumo
O Facebook tem um impacto significativo na vida de milhões de usuários da Internet, todos os dias. Entretanto, os mecanismos usados pelo Facebook para filtrar mensagens apresentadas aos usuários não são de domínio público, motivando uma engenharia reversa dos mesmos. Neste trabalho, propomos modelos e medições para melhor entender o comportamento de timelines. Em particular, reportamos resultados sobre medições de visibilidade de fontes das eleições italianas de 2018, que motivam um modelo analítico para caracterizar a visibilidade de posts. Dentre as implicações práticas de nossos estudos, indicamos seu potencial para inferir diferentes métricas de visibilidade a partir de medidas colhidas do sistema.
Referências
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Andreou, A., Venkatadri, G., Goga, O., Gummadi, K. P., Loiseau, P., and Mislove, A. (2018). Investigating ad transparency mechanisms in social media. NDSS.
Arnaudo, D. (2017). Computational Propaganda in Brazil : Social Bots during Elections.
Bakshy, E., Messing, S., and Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239):1130–1132.
Dehghan, M., Massoulie, L., Towsley, D., Menasché, D., and Tay, Y. C. (2016). A utility optimization approach to network cache design. In Proceedings - IEEE INFOCOM, volume 2016-July, pages 1–10.
Epstein, R. and Robertson, R. E. (2015). The search engine manipulation effect and its possible impact on the outcomes of elections. Nat. Academy of Sciences of the United States of America, 112(33):E4512–21.
Eslami, M., Rickman, A., Vaccaro, K., Aleyasen, A., Vuong, A., Karahalios, K., Hamilton, K., and Sandvig, C. (2015). I always assumed that I wasn’t really that close to [her]. Human Factors in Computing (HCI), pages 153–162.
Facebook (2018a). Graph api reference/user-id/home.
Facebook (2018b). Log de alterações da graph api. Retrieved April 6, 2018 from: [link].
Harchol-Balter, M. (2013). Performance Modeling and Design of Computer Systems. Cambridge University Press, Cambridge.
Martina, V., Garetto, M., and Leonardi, E. (2014). A unified approach to the performance analysis of caching systems. IEEE INFOCOM, pages 2040–2048.
O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group, New York, NY, USA.
Pariser, E. (2011). The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think. Penguin Publishing Group.
TechCruch (2016). How facebook news feed works. Retrieved December 31, 2017 from: [link].
Tsaparas, P. (2017). Online social networks and media. [link].
Woolley, S. C. and Howard, P. N. (2017). Computational propaganda worldwide: executive summary.
Publicado
26/07/2018
Como Citar
HARGREAVES, Eduardo; MENASCHÉ, Daniel; NEGLIA, Giovanni; AGOSTI, Claudio.
Visibilidade no Facebook: Modelos, Medições e Implicações. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 7. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 193-204.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2018.3591.
