Online platforms have transformed the way in which individuals access and interact with news, with a high degree of trust particularly placed in search engine results. 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. Participants' (n=280) news engagement is our proxy for familiarity, and we investigate news articles presented on Google search pages (n=1221). Our results demonstrate the steering power of the algorithmic factors on news consumption as compared to familiarity. But 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获取新闻和与新闻互动的方式,高度信任特别放在搜索引擎的结果上。我们使用网络跟踪行为数据,为期两个月,分析三个竞争因素,两个算法(级别和代表性)和一个心理(形象),它们可能影响搜索结果中出现的新闻文章的选择。参与者(n=280)的新闻报道是我们熟悉的代理,我们调查谷歌搜索网页(n=1221)上发布的新闻文章。我们的结果显示,与熟悉相比,算法因素对新闻消费具有指导力。但是,尽管排名的影响很大,但我们发现,与非新新闻相比,它对于新闻文章的作用较小。我们确认,谷歌搜索促使个人接触不熟悉的信息来源,并发现它增加了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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