Citation

Menghini C, Anagnostopoulos A, Upfal E. arXiv 2021; 2021: 2007.08197.

Copyright

(Copyright © 2021, The author(s), Publisher Cornell University Library)

DOI

unavailable

PMID

unavailable

Abstract

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.

Language: en

Keywords

Computer Science - Computers and Society; Computer Science - Social and Information Networks