Abstract

We introduce a design that is able to face some of the challenges that digital news consumption is posing to traditional media effects methods like linkage analysis. The challenges include (1) memory errors and biases when reporting everyday news media consumption leading to (2) errors when linking mass media outlets to survey data; (3) personalization of media content, as well as (4) short-term dynamic processes. Mobile Intensive Longitudinal Linkage Analysis (MILLA) uses an innovative combination of smartphone data donations to capture media exposure and relevant media content, a mobile experience sampling questionnaire to capture immediate reactions to news, and the content analysis of uploaded news media content to measure media effects. The design is explained by using an example of negativity in the news and its effects on emotional reactions of recipients.

摘要

我们介绍了一种能够应对数字新闻消费对传统媒体效果方法（如关联分析）带来的一些挑战的设计。挑战包括 (1) 报道日常新闻媒体消费时的记忆错误和偏见，导致 (2) 将大众媒体与调查数据联系起来时出现错误；(3) 媒体内容的个性化，以及 (4) 短期动态过程。Mobile Intensive Longitudinal Linkage Analysis (MILLA) 使用智能手机数据捐赠的创新组合来捕捉媒体曝光和相关媒体内容，使用移动体验抽样调查问卷来捕捉对新闻的即时反应，以及对上传的新闻媒体内容的内容分析来衡量媒体效果.