论文标题
搜索引擎对新闻消费的影响：排名和代表性超过新闻选择的熟悉程度
Search engine effects on news consumption: ranking and representativeness outweigh familiarity in news selection
论文作者
论文摘要
在线平台改变了个人访问和与新闻互动的方式，尤其是在搜索引擎结果中放置的高度信任。我们在2个月的时间内使用Web跟踪的行为数据，并分析了三个竞争因素，两个算法（排名和代表性）以及一个可以影响搜索结果中出现的新闻文章的心理（熟悉）。参与者（n = 280）新闻参与是我们的熟悉程度的代理，我们调查了Google搜索页面上介绍的新闻文章（n = 1221）。我们的结果证明了与熟悉度相比，新闻消费算法因素的转向动力。但是，尽管排名很强，但我们发现与非新闻相比，它在新闻文章中起着较小的作用。我们确认Google搜索驱使个人不熟悉的资源，并发现它增加了政治受众对新闻来源的多样性。通过我们的方法，我们迈出了一步，应对在算法塑造的数字环境中测试社会科学理论的挑战。
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.
