Abstract

This study aims to predict audience-rated news quality with journalistic values and linguistic/formal features of news articles, based on the theoretical rationales derived from information processing models, journalism and news consumption literature, and linguistic studies. We employed a traditional social science survey of over 7,800 news audiences and implemented natural language processing, text-mining, and neural network analyses for 1,500 news articles concerning public affairs. Results suggest that the journalistic values of news articles are stronger predictors of audience-rated news quality than their linguistic/formal features. The impact of journalistic values overrode that of the news audience attributes which served as a baseline for comparison. Specifically, believability, depth, and diversity were more important in predicting audience-rated news quality than readability, objectivity, factuality, and sensationalism. Regarding linguistic/formal features, bylines, sources, subjective expressions, and article similarities were influential. This study provides an additional support that news audiences regard journalistic values highly as substantial factors of news quality. It also provides empirical evidence for the normative news reporting guidelines. Methodologically, it serves as an example of integrating computational and textual methods with traditional social science approach.

摘要

这项研究旨在根据从信息处理模型，新闻和新闻消费文献以及语言学研究得出的理论原理，预测具有新闻价值和新闻文章的语言/形式特征的受众评价新闻质量。我们对7800多个新闻受众进行了传统的社会科学调查，并对1,500篇有关公共事务的新闻进行了自然语言处理，文本挖掘和神经网络分析。结果表明，新闻文章的新闻价值比其语言/形式特征更能预测受众评价的新闻质量。新闻价值的影响超越了作为比较基准的新闻受众属性的影响。具体来说，可信度，深度，在预测受众评价的新闻质量方面，多样性和多样性比可读性，客观性，事实性和轰动性更为重要。关于语言/形式特征，署名，来源，主观表达和文章相似性都具有影响力。这项研究为新闻观众将新闻价值高度视为新闻质量的重要因素提供了额外的支持。它还为规范性新闻报道指南提供了经验证据。从方法上讲，它是将计算和文本方法与传统社会科学方法相结合的一个示例。这项研究为新闻观众将新闻价值高度视为新闻质量的重要因素提供了额外的支持。它还为规范性新闻报道指南提供了经验证据。从方法上讲，它是将计算和文本方法与传统社会科学方法相结合的一个示例。这项研究为新闻观众将新闻价值高度视为新闻质量的重要因素提供了额外的支持。它还为规范性新闻报道指南提供了经验证据。从方法上讲，它是将计算和文本方法与传统社会科学方法相结合的一个示例。