Modeling, auditing, and forecasting Facebook publisher visibilities: A case study in Italian elections
DOI:
https://doi.org/10.5753/isys.2019.601Keywords:
social networks, facebook, bias, measurements, forecast, temporal seriesAbstract
Facebook has a significant impact on the lives of billions of Internet users, every day. However, the algorithms used by Facebook to filter messages presented to users are not in the public domain. In this work, we propose models, experiments and time series analysis to fill this gap. As a case study, we conducted experiments with the Facebook News Feed during the 2018 Italian presidential elections. Among the implications of our studies, we indicated the potential of the proposed model to accurately infer different visibility metrics, the ability to audit News Facebook feed and the power to forecast timeline occupancies given their previously observed values.
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References
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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., 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.
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Published
2019-09-18
How to Cite
Hargreaves, E. M., Menasché, D. S., Neglia, G., & Agosti, C. (2019). Modeling, auditing, and forecasting Facebook publisher visibilities: A case study in Italian elections. ISys - Brazilian Journal of Information Systems, 12(3), 139–159. https://doi.org/10.5753/isys.2019.601
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Extended versions of selected articles
