Online platforms have transformed the ways in which individual access and interact with news. For example, individuals put a high degree of trust in search engines. We use web-tracked behavioral data across a 2-month period and analyze three competing factors, two algorithmic (ranking and representativeness) and one psychological (familiarity), that could influence the selection of news articles that appear in search results. Using news engagement as a proxy for familiarity, and Google search pages (n=1221) that led participants (n=280) to news articles, our results demonstrate the steering power of the algorithmic factors on news consumption as compared to familiarity. Despite the strong effect of ranking, we find that it plays a lesser role for news articles compared to non-news. We confirm that Google Search drives individuals to unfamiliar sources and find that it increases the diversity of the political audience to news sources. With our methodology, we take a step in tackling the challenges of testing social science theories in digital contexts shaped by algorithms.
翻译：在线平台改变了个人访问和与新闻互动的方式。例如,个人高度信任搜索引擎。我们使用网络跟踪的行为数据,为期两个月,分析三种竞争因素:两种算法(级别和代表性)和一种心理(形象),这可能影响搜索结果中出现的新闻文章的选择。利用新闻参与作为熟悉信息的代理,以及谷歌搜索网页(n=1221),导致参与者(n=280)进入新闻文章,我们的结果显示了算法因素对新闻消费的指导力,而不是熟悉程度。尽管排名的影响很大,但我们发现,与非新闻相比,它对于新闻文章的作用较小。我们确认,谷歌搜索可以推动个人对不熟悉的信息来源,并发现它会增加政治受众对新闻来源的多样性。用我们的方法,我们在应对在由算法塑造的数字环境中测试社会科学理论的挑战方面迈出了一步。
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