Marking biased texts is a practical approach to increase media bias awareness among news consumers. However, little is known about the generalizability of such awareness to new topics or unmarked news articles, and the role of machine-generated bias labels in enhancing awareness remains unclear. This study tests how news consumers may be trained and pre-bunked to detect media bias with bias labels obtained from different sources (Human or AI) and in various manifestations. We conducted two experiments with 470 and 846 participants, exposing them to various bias-labeling conditions. We subsequently tested how much bias they could identify in unlabeled news materials on new topics. The results show that both Human (t(467) = 4.55, p < .001, d = 0.42) and AI labels (t(467) = 2.49, p = .039, d = 0.23) increased correct detection compared to the control group. Human labels demonstrate larger effect sizes and higher statistical significance. The control group (t(467) = 4.51, p < .001, d = 0.21) also improves performance through mere exposure to study materials. We also find that participants trained with marked biased phrases detected bias most reliably (F(834,1) = 44.00, p < .001, {\eta}2part = 0.048). Our experimental framework provides theoretical implications for systematically assessing the generalizability of learning effects in identifying media bias. These findings also provide practical implications for developing news-reading platforms that offer bias indicators and designing media literacy curricula to enhance media bias awareness.

翻译：标注有偏见的文本是提高新闻消费者媒体偏见意识的一种实用方法。然而，此类意识对新主题或未标注新闻文章的泛化能力尚不清楚，且机器生成的偏见标签在提升意识方面的作用仍不明确。本研究测试了如何通过不同来源（人工或AI）和不同呈现形式的偏见标签来训练和预先防范新闻消费者，以检测媒体偏见。我们进行了两项实验，分别涉及470名和846名参与者，让他们接触各种偏见标注条件。随后，我们测试了他们在未标注的新主题新闻材料中能识别出多少偏见。结果显示，与对照组相比，人工标注（t(467) = 4.55, p < .001, d = 0.42）和AI标注（t(467) = 2.49, p = .039, d = 0.23）均提高了正确检测率。人工标注表现出更大的效应量和更高的统计显著性。对照组（t(467) = 4.51, p < .001, d = 0.21）仅通过接触研究材料也提高了表现。我们还发现，使用标注的偏见短语进行训练的参与者检测偏见最为可靠（F(834,1) = 44.00, p < .001, {\eta}2part = 0.048）。我们的实验框架为系统评估识别媒体偏见的学习效果泛化性提供了理论启示。这些发现也为开发提供偏见指标的新闻阅读平台和设计旨在提升媒体偏见意识的媒介素养课程提供了实践指导。