Bias and polarization are not just about placing misinformation on the Web but also involve concerted efforts to change how we navigate it. One of the strongest points of Wikipedia is to allows readers to easily navigate a topic, through its hyperlinks structure. Thus, it is crucial to ensure a user to have the same probability of being exposed to knowledge that expresses different viewpoints concerning the given topic. In this work, we investigate whether the topology and polarization of a topic-induced-graph (e.g. U.S. Politics induced network) has an impact on users’ navigation paths making them biased toward one of the possible topic perspectives. Modeling users behaviour and exploiting Wikipedia clickstreams, we analyze users exposure to different leaning during their sessions, thus the chance of being trapped within a knowledge bubble presenting a unique viewpoint about the topic, and differences among users that start their navigation from articles representing different perspectives.
2019, 2019 IEEE International Conference on Big Data (Big Data), Pages 6154-6156
Wikipedia Polarization and Its Effects on Navigation Paths (04b Atto di convegno in volume)
Menghini Cristina, Anagnostopoulos Aris, Upfal Eli
Gruppo di ricerca: Algorithms and Data Science