SSLE: A framework for evaluating the "Filter Bubble" effect on the news aggregator and recommenders
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Wang, Can
Zhao, Yunwei
Shu, Min
Wang, Wenlei
Min, Yong
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Abstract
Recommendation algorithms are data filtering tools that make use of algorithms and data to recommend the most relevant items to a particular user. The algorithm-driven recommenders become indispensable and supersede search engines as the most important information dissemination channel. On one hand, it becomes an integral component in the existing social media, e.g. Weibo, Twitter, etc. On the other hand, news aggregators and recommenders have proliferated and gained an increasing market share. As a result, the previous studies usually study the “filter bubbles” phenomenon in the context where the social filtering dominates the dissemination of information. However, less attention is paid to the news aggregators and recommenders where algorithm-driven technological filtering dominates. Therefore, in the previous research, “filter bubbles” are usually equated with the community structure, but lack of the detailed analysis of the content agglomeration through the users’ interaction with the platforms. Based on these concerns, we propose a four-phase (“Selection”, “Setup”, “Link”, and “Evaluation”) skeletal solution framework targeted at exploiting the filter bubble effect of the personalized news aggregation and recommendation system. Furthermore, we illustrate the effectiveness of the proposed framework with a case study in three top Chinese news aggregators, i.e. Toutiao, Baidu News, and Tencent News. The results show that the users are narrowed into one or a limited number of topics over time. The phenomenon of the narrowed topics is deemed as the emergence of the “filter bubbles”. We also observe that the filter bubbles demonstrate different convergence degrees as user’s individual preference varies.
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World Wide Web
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25
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3
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© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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Science & Technology
Technology
Computer Science, Information Systems
Computer Science, Software Engineering
Computer Science
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Han, H; Wang, C; Zhao, Y; Shu, M; Wang, W; Min, Y, SSLE: A framework for evaluating the "Filter Bubble" effect on the news aggregator and recommenders, World Wide Web, 2022, 25 (3), pp. 1169-1195