Modelando, Auditando e Prevendo a Visibilidade de Fontes no Facebook: Um Estudo de Caso nas Eleições Italianas
DOI:
https://doi.org/10.5753/isys.2019.601Keywords:
Redes sociais, Facebook, viés, medições, previsões, séries temporaisAbstract
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.
Downloads
Não há dados estatísticos.
Referências
Altman, E., Kumar, P., Venkatramanan, S., and Kumar, A. (2013). Competition over timeline in social networks. ASONAM, pages 1352–1357.
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., Menasch´e, 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 ofSciences ofthe United States ofAmerica, 112(33):E4512–21.
Eslami, M., Rickman, A., Vaccaro, K., Aleyasen, A., Vuong, A., Karahalios, K., Hamil- ton, 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 alterac¸ ˜oes da graph api. Retrieved April 6, 2018 from: https://developers.facebook.com/docs/graph-api/changelog. 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: https://techcrunch.com/2016/09/06/ ultimate-guide-to-the-news-feed/.
Tsaparas, P. (2017). Online social networks and media. http://www.cs.uoi.gr/ ˜tsap/teaching/cs-l14/references.html.
Woolley, S. C. and Howard, P. N. (2017). Computational propaganda worldwide: execu- tive summary.
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., Menasch´e, 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 ofSciences ofthe United States ofAmerica, 112(33):E4512–21.
Eslami, M., Rickman, A., Vaccaro, K., Aleyasen, A., Vuong, A., Karahalios, K., Hamil- ton, 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 alterac¸ ˜oes da graph api. Retrieved April 6, 2018 from: https://developers.facebook.com/docs/graph-api/changelog. 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: https://techcrunch.com/2016/09/06/ ultimate-guide-to-the-news-feed/.
Tsaparas, P. (2017). Online social networks and media. http://www.cs.uoi.gr/ ˜tsap/teaching/cs-l14/references.html.
Woolley, S. C. and Howard, P. N. (2017). Computational propaganda worldwide: execu- tive summary.
Downloads
Published
2019-09-18
Como Citar
Hargreaves, E. M., Menasché, D. S., Neglia, G., & Agosti, C. (2019). Modelando, Auditando e Prevendo a Visibilidade de Fontes no Facebook: Um Estudo de Caso nas Eleições Italianas. ISys - Revista Brasileira De Sistemas De Informação, 12(3), 139–159. https://doi.org/10.5753/isys.2019.601
Issue
Section
Versões estendidas de artigos selecionados
