Abstract
The relative hostile media effect suggests that partisans tend to perceive the bias of slanted news differently depending on whether the news is slanted in favor of or against their sides. To explore the effect of an algorithmic vs. human source on hostile media perceptions, this study conducts a 3 (author attribution: human, algorithm, or human-assisted algorithm) x 3 (news attitude: pro-issue, neutral, or anti-issue) mixed factorial design online experiment (<em>N</em> = 511). This study uses a transformer-based adversarial network to auto-generate comparable news headlines. The framework was trained with a dataset of 364,986 news stories from 22 mainstream media outlets. The results show that the relative hostile media effect occurs when people read news headlines attributed to all types of authors. News attributed to a sole human source is perceived as more credible than news attributed to two algorithm-related sources. For anti-Trump news headlines, there exists an interaction effect between author attribution and issue partisanship while controlling for people’s prior belief in machine heuristics. The difference of hostile media perceptions between the two partisan groups was relatively larger in anti-Trump news headlines compared with pro-Trump news headlines.
Highlights
With advances in machine learning techniques and the growing availability of big data, algorithms have become widely adopted in news agencies around the world (Jia & Johnson, 2021)
One recent meta‐analysis shows that when reading the actual con‐ tent written by humans and algorithms, people per‐ ceive no difference in terms of news credibility; how‐ ever, people perceive news purportedly attributed to algorithms as slightly less credible than news attributed to humans (Graefe & Bohlken, 2020)
Our study introduced a novel type of source and shed light on new trends in the era of an algorithms‐driven society
Summary
With advances in machine learning techniques and the growing availability of big data, algorithms have become widely adopted in news agencies around the world (Jia & Johnson, 2021). Automated journalism is defined as a form of news production that can automatically produce news stories with little human intervention beyond the initial programming phase (Carlson, 2015; Graefe, 2016; Tandoc et al, 2020). Automated journalistic writing is mostly restricted to factual and data‐driven top‐ ics such as sports, finance, crime, weather, and disaster reporting, it has been applied to other domains such as political news (Jia & Johnson, 2021; Wu, 2020). With the growing presence of automated journalism, this new technological affordance has altered how audiences con‐ sume and engage with news (Liu & Wei, 2019).
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