This study investigates how cultural proximity affects the ability to detect AI-generated fake news by comparing South African participants with those from other nationalities. As large language models increasingly enable the creation of sophisticated fake news, understanding human detection capabilities becomes crucial, particularly across different cultural contexts. We conducted a survey where 89 participants (56 South Africans, 33 from other nationalities) evaluated 10 true South African news articles and 10 AI-generated fake versions. Results reveal an asymmetric pattern: South Africans demonstrated superior performance in detecting true news about their country (40% deviation from ideal rating) compared to other participants (52%), but performed worse at identifying fake news (62% vs. 55%). This difference may reflect South Africans' higher overall trust in news sources. Our analysis further shows that South Africans relied more on content knowledge and contextual understanding when judging credibility, while participants from other countries emphasised formal linguistic features such as grammar and structure. Overall, the deviation from ideal rating was similar between groups (51% vs. 53%), suggesting that cultural familiarity appears to aid verification of authentic information but may also introduce bias when evaluating fabricated content. These insights contribute to understanding cross-cultural dimensions of misinformation detection and inform strategies for combating AI-generated fake news in increasingly globalised information ecosystems where content crosses cultural and geographical boundaries.

翻译：本研究通过比较南非参与者与其他国籍的参与者，探讨文化亲近性如何影响检测AI生成虚假新闻的能力。随着大语言模型日益能够生成复杂的虚假新闻，理解人类的检测能力变得至关重要，尤其是在不同的文化背景下。我们进行了一项调查，89名参与者（56名南非人，33名其他国籍）评估了10篇真实的南非新闻文章和10篇AI生成的虚假版本。结果显示了一种不对称模式：南非人在检测关于自己国家的真实新闻方面表现出更优的性能（与理想评分的偏差为40%），而其他参与者为52%，但在识别虚假新闻方面表现更差（62% vs. 55%）。这种差异可能反映了南非人对新闻来源的总体信任度更高。我们的分析进一步表明，南非人在判断可信度时更多地依赖内容知识和上下文理解，而其他国家的参与者则强调形式语言特征，如语法和结构。总体而言，两组与理想评分的偏差相似（51% vs. 53%），这表明文化熟悉度似乎有助于验证真实信息，但在评估捏造内容时也可能引入偏见。这些见解有助于理解错误信息检测的跨文化维度，并为在内容跨越文化和地理边界的日益全球化的信息生态系统中打击AI生成的虚假新闻提供策略参考。