Predicting Audience-Rated News Quality: Using Survey, Text Mining, and Neural Network Methods

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.