The most important feature of Wikipedia is the presence of hyperlinks in pages. Link placement is the product of people's collaboration, consequently Wikipedia naturally inherits human bias. Due to the high influence that links' disposition has on users' navigation sessions, one needs to verify that, given a controversial topic, the  hyperlinks' network does not expose users to only one side of the subject. A Wikipedia's topic-induced network that prevents users the discovery of different facets of an issue, suffers from structural bias. In thiswork, we define the static structural bias, which indicates if the strength of connections between pages of contrasting inclinations is the same, and the dynamic structural bias, which quantifies the network's level bias that users face over the course of their navigation sessions. Our measurements of structural bias on several controversial topics demonstrate its existence, revealing that users have low likelihood of reaching pages of opposing inclination from where they start, and that they navigate Wikipedia showing a behaviour much more biased than the expected from the baselines. Our findings advance the relevance of the problem and pave the way for developing systems that automatically measure and propose hyperlink locations that minimize the presence and effects of structural bias .
People eager to learn about a topic can access Wikipedia to form a preliminary opinion. Despite the solid revision process behind the encyclopedia's articles, the users' exploration process is still influenced by the hyperlinks' network . In this paper, we shed light on this overlooked phenomenon by investigating how articles describing complementary subjects of a topic interconnect, and thus may shape readers' exposure to diverging content. To quantify this, we introduce the exposure to diverse information, a metric that captures how users' exposure to multiple subjects of a topic varies click-after-click by leveraging navigation models. For the experiments, we collected six topic-induced networks about polarizing topics and analyzed the extent to which their topologies induce readers to examine diverse content. More specifically, we take two sets of articles about opposing stances (e.g., guns control and guns right) and measure the probability that users move within or across the sets, by simulating their behavior via a Wikipedia-tailored model. Our findings show that the networks hinder users to symmetrically explore diverse content. Moreover, on average, the probability that the networks nudge users to remain in a knowledge bubble is up to an order of magnitude higher than that of exploring pages of contrasting subjects. Taken together, those findings return a new and intriguing picture of Wikipedia's network structural influence on polarizing issues' exploration .
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The most important feature of Wikipedia is the presence of hyperlinks in pages. Link placement is the product of people's collaboration, consequently Wikipedia naturally inherits human bias. Due to the high influence that links' disposition has on users' navigation sessions, one needs to verify that, given a controversial topic,
People eager to learn about a topic can access Wikipedia to form a preliminary opinion. Despite the solid revision process behind the encyclopedia's articles,
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users' exploration process is still influenced by the
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does not expose users to only one side of the subject. A Wikipedia's
. In this paper, we shed light on this overlooked phenomenon by investigating how articles describing complementary subjects of a topic interconnect, and thus may shape readers' exposure to diverging content. To quantify this, we introduce the exposure to diverse information, a metric that captures how users' exposure to multiple subjects of a topic varies click-after-click by leveraging navigation models. For the experiments, we collected six
R
network that prevents users the discovery of different facets of an issue, suffers from structural bias. In thiswork, we define the static structural bias, which indicates if the strength of connections between pages of contrasting inclinations is the same, and the dynamic structural bias, which quantifies the network's level bias that users face over the course of their navigation sessions. Our measurements of structural bias on several controversial topics demonstrate its existence, revealing that users have low likelihood of reaching pages of opposing inclination from where they start, and that they navigate Wikipedia showing a behaviour much more biased than the expected from the baselines. Our findings advance the relevance of the problem and pave the way for developing systems that automatically measure and propose hyperlink locations that minimize the presence and effects of structural bias
networks about polarizing topics and analyzed the extent to which their topologies induce readers to examine diverse content. More specifically, we take two sets of articles about opposing stances (e.g., guns control and guns right) and measure the probability that users move within or across the sets, by simulating their behavior via a Wikipedia-tailored model. Our findings show that the networks hinder users to symmetrically explore diverse content. Moreover, on average, the probability that the networks nudge users to remain in a knowledge bubble is up to an order of magnitude higher than that of exploring pages of contrasting subjects. Taken together, those findings return a new and intriguing picture of Wikipedia's network structural influence on polarizing issues' exploration