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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 \u201cfilter bubbles\u201d 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, \u201cfilter bubbles\u201d are usually equated with the community structure, but lack of the detailed analysis of the content agglomeration through the users\u2019 interaction with the platforms. Based on these concerns, we propose a four-phase (\u201cSelection\u201d, \u201cSetup\u201d, \u201cLink\u201d, and \u201cEvaluation\u201d) 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 \u201cfilter bubbles\u201d. We also observe that the filter bubbles demonstrate different convergence degrees as user\u2019s individual preference varies.<\/jats:p>","DOI":"10.1007\/s11280-022-01031-4","type":"journal-article","created":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T08:02:38Z","timestamp":1646726558000},"page":"1169-1195","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["SSLE: A framework for evaluating the \u201cFilter Bubble\u201d effect on the news aggregator and recommenders"],"prefix":"10.1007","volume":"25","author":[{"given":"Han","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Can","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunwei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Shu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenlei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Min","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,8]]},"reference":[{"issue":"5","key":"1031_CR1","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1109\/TKDE.2011.15","volume":"24","author":"G Adomavicius","year":"2012","unstructured":"Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. 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