There are growing concerns that social media exacerbates the selective exposure of audience members to content that supports their political views. However, despite the hype, the existing literature does not fully address the extent to which social media users selectively consume like-minded news stories, in part due to different methodologies. In an attempt to move toward a common framework, this study examined the partisan selective exposure of a representative sample of Twitter users by combining survey data with digital trace data. Specifically, the study linked survey responses (n = 558) from Twitter users with their media following and exposure to news via their friends. The study found that selectivity bias was present in all types of data, including self-reported media consumption (survey), media following (Twitter), and indirect exposure to media (Twitter). However, the study found some differences between self-reports and digital measures such that the overlaps in media diets between partisan groups were much larger based on the digital trace data than the self-reported data. In addition, the study observed an asymmetric pattern of selective exposure between conservatives and liberals in the digital trace data, but not in the self-reported data. The implications of these findings are discussed with reference to the contemporary news environment, hostile media effects, and normative assumptions of selective exposure.

人们越来越担心社交媒体会加剧受众成员对支持其政治观点的内容的选择性曝光。然而，尽管进行了大肆宣传，但现有文献并未完全解决社交媒体用户选择性地消费志趣相投的新闻故事的程度，部分原因是方法不同。为了向通用框架迈进，本研究通过将调查数据与数字跟踪数据相结合，研究了有代表性的Twitter用户样本的党派选择性暴露。具体来说，该项研究调查相连的反应（ñ = 558）来自Twitter用户，他们关注媒体并通过其朋友接触新闻。研究发现，所有类型的数据都存在选择性偏差，包括自我报告的媒体消费（调查），媒体关注（Twitter）和间接接触媒体（Twitter）。但是，该研究发现自我报告与数字测量之间存在一些差异，因此基于数字跟踪数据，游击队之间媒体饮食的重叠比自我报告的数据要大得多。此外，该研究在数字跟踪数据中观察到保守派和自由主义者之间选择性暴露的不对称模式，而在自我报告的数据中则没有。这些发现的含义将参考当代新闻环境，敌对媒体的影响以及选择性曝光的规范假设进行讨论。