TY - GEN
T1 - The BiasChecker
T2 - 13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
AU - Yang, Can
AU - Nunes, Bernardo Pereira
AU - Santos, Jônatas Castro Dos
AU - Siqueira, Sean Wolfgand Matsui
AU - Xu, Xinyuan
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/11/8
Y1 - 2021/11/8
N2 - 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.
AB - 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.
KW - filter bubble
KW - personalisation algorithms
KW - search mechanism
KW - social media
UR - http://www.scopus.com/inward/record.url?scp=85124409636&partnerID=8YFLogxK
U2 - 10.1145/3487351.3489482
DO - 10.1145/3487351.3489482
M3 - Conference contribution
T3 - Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
SP - 305
EP - 308
BT - Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021
A2 - Coscia, Michele
A2 - Cuzzocrea, Alfredo
A2 - Shu, Kai
PB - Association for Computing Machinery, Inc
Y2 - 8 November 2021
ER -