The BiasChecker: How biased are social media searches?
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Yang, Can
Xu, Xinyuan
Pereira Nunes, Bernardo
Dos Santos, Jonatas C
Siqueira, S
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Association for Computing Machinery (ACM)
Abstract
Social media searches are frequently employed by users to keep them up to date about ongoing events and learn broadly about public opinion on topics that are unfamiliar to them. Nevertheless, there are rising concerns about the results returned that can reinforce users' existing biases - the inclination to one opinion over another. This paper introduces a tool, called BiasChecker, that contributes to the check for bias in search results on a social media platform. BiasChecker follows a distributed and extendable architecture that allows us to simulate users following and unfollowing accounts, search for different polarised topics in a concurrent manner and measure bias. It may be applied to multiple social media platforms. The proposed tool takes into account several factors that can interfere with the detection of bias, e.g., the cross-over effect, geolocation, IP address, and language.
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ASONAM '21: Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Open Access via publisher website
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Restricted until
2099-12-31
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