Abstract
This meta-analysis summarizes evidence on how readers perceive the credibility, quality, and readability of automated news in comparison to human-written news. Overall, the results, which are based on experimental and descriptive evidence from 12 studies with a total of 4,473 participants, showed no difference in readers’ perceptions of credibility, a small advantage for human-written news in terms of quality, and a huge advantage for human-written news with respect to readability. Experimental comparisons further suggest that participants provided higher ratings for credibility, quality, and readability simply when they were told that they were reading a human-written article. These findings may lead news organizations to refrain from disclosing that a story was automatically generated, and thus underscore ethical challenges that arise from automated journalism.
Highlights
Automated journalism, sometimes referred to as algorithmic journalism (Dörr, 2016) or robot journalism (Clerwall, 2014), alludes to the method by which algorithms are used to automatically generate news stories from structured, machine-readable data (Graefe, 2016).The idea of news automation is not new
There was no difference in how readers perceived the credibility of human-written and automated news (d = 0.0; SE = 0.02)
For both credibility (d = 0.3; SE = 0.03) and quality (d = 0.8; SE = 0.03), experimental evidence favored human-written over automated news
Summary
Sometimes referred to as algorithmic journalism (Dörr, 2016) or robot journalism (Clerwall, 2014), alludes to the method by which algorithms are used to automatically generate news stories from structured, machine-readable data (Graefe, 2016).The idea of news automation is not new. Half a century ago, Glahn (1970) described a process for automatically generating, what he called, “computer-produced worded weather forecasts.” His idea was to create pre-written statements that describe different weather conditions, each of which corresponds to a particular output of a weather forecasting model (e.g., the combination of wind speed, precipitation, and temperature). This process is similar to today’s template-based solutions offered by software providers in which a set of predefined rules are used to determine which prewritten statements are selected to create a story (Graefe, 2016). In 2014, when the Associated Press gained much public attention for the decision to automate earnings reports (White, 2015), Thomson Financial (today part of Thomson Reuters) had already been automating such stories for nearly a decade (van Duyn, 2006)
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