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.

中文翻译：

---

算法还是人力资源？使用基于 Transformer 的框架检查相对敌对媒体效果

相对敌对的媒体效应表明，党派人士倾向于对倾斜新闻的偏见有不同的看法，这取决于新闻是倾向于还是反对他们的一方。为了探索算法与人类来源对敌对媒体感知的影响，本研究进行了 3（作者归属：人类、算法或人类辅助算法）x 3（新闻态度：支持问题、中立或反问题）混合因子设计在线实验（<em>N</em> = 511）。本研究使用基于变压器的对抗网络自动生成可比较的新闻标题。该框架使用来自 22 个主流媒体的 364,986 个新闻故事的数据集进行训练。结果表明，当人们阅读归因于所有类型作者的新闻标题时，会发生相对敌对的媒体效应。归因于单一人类来源的新闻被认为比归因于两个与算法相关的来源的新闻更可信。对于反特朗普的新闻头条，在控制人们对机器启发式的先验信念的同时，作者归属和问题党派之间存在交互作用。与亲特朗普的新闻头条相比，反特朗普的新闻头条在两个党派团体之间的敌对媒体认知差异相对较大。