What is (automated) news?

A content analysis of algorithm-written news articles

Abstract

The use of automation in producing news articles confronts journalism with threats, opportunities, and ambiguities. Thus, automation in journalism has attracted a lot of attention, from scholars who sought the perspective of human journalists to those who examined how audiences process algorithm-written news articles. These studies assume that human-written news articles differ from automated-written news articles. But do they? This current study compared human-written with algorithm-written news articles published by media and software company Bloomberg. Guided by the frameworks of field theory and journalistic boundaries, we compared the news articles based on traditional markers of human-written news. Using manual content analysis, we found that algorithm-written news shares some similarities with human-written news, such as focusing on timely or recent events and using the inverted pyramid format. Beyond these, we also found differences. First, in terms of news values, human-written news articles tend to display more negativity and impact than algorithm-written news articles. Human-written news articles are also more likely to include interpretation while algorithm-written articles tend to be shorter and contain no human sources.