Burst of the Filter Bubble?
Burst of the Filter Bubble?
复制标题
过滤器气泡破裂？
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
H. Brosius
中科院分区：
文献类型：
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作者：
Mario Haim;A. Graefe;H. Brosius
在提供针对用户的个人兴趣的个性化内容时，推荐系统被假设为减少新闻多样性，从而导致部分信息盲（即，过滤气泡）。我们进行了两项探索性研究，以测试内隐和外显个性化对Google新闻内容和来源多样性的影响。除了内隐个性化对内容多样性的小影响外，我们没有发现对过滤气泡假说的支持。然而，我们确实发现了一个普遍的偏见，即谷歌新闻过度代表某些新闻媒体，而代表其他频繁出现的新闻媒体。这些结果增加了越来越多的证据，表明对在线新闻背景下算法过滤泡沫的担忧可能被夸大了。
In offering personalized content geared toward users’ individual interests, recommender systems are assumed to reduce news diversity and thus lead to partial information blindness (i.e., filter bubbles). We conducted two exploratory studies to test the effect of both implicit and explicit personalization on the content and source diversity of Google News. Except for small effects of implicit personalization on content diversity, we found no support for the filter-bubble hypothesis. We did, however, find a general bias in that Google News over-represents certain news outlets and under-represents other, highly frequented, news outlets. The results add to a growing body of evidence, which suggests that concerns about algorithmic filter bubbles in the context of online news might be exaggerated.