ORCID Profile
0000-0001-6785-1203
Current Organisations
University of Technology Sydney
,
University of Tasmania
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Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 2023
Publisher: Elsevier BV
Date: 02-2023
Publisher: Elsevier BV
Date: 03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: ACM
Date: 18-07-2023
Publisher: Springer International Publishing
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 21-03-2018
Publisher: Elsevier BV
Date: 05-2023
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2022
Abstract: AI recommendation techniques provide users with personalized services, feeding them the information they may be interested in. The increasing personalization raises the hypotheses of the "filter bubble" and "echo chamber" effects. To investigate these hypotheses, in this paper, we inspect the impact of recommendation algorithms on forming two types of ideological isolation, i.e., the in idual isolation and the topological isolation, in terms of the filter bubble and echo chamber effects, respectively. Simulation results show that AI recommendation strategies severely facilitate the evolution of the filter bubble effect, leading users to become ideologically isolated at an in idual level. Whereas, at a topological level, recommendation algorithms show eligibility in connecting in iduals with dissimilar users or recommending erse topics to receive more erse viewpoints. This research sheds light on the ability of AI recommendation strategies to temper ideological isolation at a topological level.
No related grants have been discovered for Shiqing Wu.