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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.
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Understanding dynamics of polarization via multiagent social simulation
Abstract It is widely recognized that the Web contributes to user polarization, and such polarization affects not just politics but also peoples’ stances about public health, such as vaccination. Understanding polarization in social networks is challenging because it depends not only on user attitudes but also their interactions and exposure to information. We adopt Social Judgment Theory to operationalize attitude shift and model user behavior based on empirical evidence from past studies. We design a social simulation to analyze how content sharing affects user satisfaction and polarization in a social network. We investigate the influence of varying tolerance in users and selectively exposing users to congenial views. We find that (1) higher user tolerance slows down polarization and leads to lower user satisfaction; (2) higher selective exposure leads to higher polarization and lower user reach; and (3) both higher tolerance and higher selective exposure lead to a more homophilic social network.
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- Award ID(s):
- 1908374
- PAR ID:
- 10454895
- Date Published:
- Journal Name:
- AI & SOCIETY
- Volume:
- 38
- Issue:
- 4
- ISSN:
- 0951-5666
- Page Range / eLocation ID:
- 1373 to 1389
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s00146-022-01626-5
