@inproceedings{2fffcdf211794ac6a555d26ce4d7314a,
title = "The BiasChecker: How biased are social media searches?",
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.",
keywords = "filter bubble, personalisation algorithms, search mechanism, social media",
author = "Can Yang and Nunes, \{Bernardo Pereira\} and Santos, \{J{\^o}natas Castro Dos\} and Siqueira, \{Sean Wolfgand Matsui\} and Xinyuan Xu",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021 ; Conference date: 08-11-2021",
year = "2021",
month = nov,
day = "8",
doi = "10.1145/3487351.3489482",
language = "English",
series = "Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021",
publisher = "Association for Computing Machinery (ACM)",
pages = "305--308",
editor = "Michele Coscia and Alfredo Cuzzocrea and Kai Shu",
booktitle = "Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021",
address = "United States",
}