跨文化对比发现，南非人更易识别真新闻但难辨假新闻。
A Cross-Cultural Assessment of Human Ability to Detect LLM-Generated Fake News about South Africa
- 比较南非与他国参与者对南非法新闻的真假判断能力。
- 南非人识别真新闻准确率高（偏差40%），但辨假能力弱（偏差62%）。
- 文化熟悉度助真伪识别，但可能带来认知偏见，适合政策制定者参考。
本研究通过对比南非人与其他国籍参与者，探究文化接近性对检测大语言模型生成假新闻的影响。共招募89名参与者（56名南非人，33名其他国籍），评估10篇真实南非法新闻及10篇AI生成假新闻。结果显示：南非人对本国真实新闻的判断偏差为40%，优于其他参与者（52%）；但在识别假新闻时表现更差（偏差62%对55%）。这可能源于南非人对新闻来源更高的整体信任。分析表明，南非人更依赖内容知识与语境理解，而他国参与者更关注语法与结构等形式特征。两组整体偏离理想评分程度相近（51%对53%），说明文化熟悉度有助于验证真实信息，却可能加剧对伪造内容的误判。研究为理解跨文化虚假信息检测提供依据，助力应对全球化信息生态中的AI假新闻挑战。
原文摘要 · Abstract (English)
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.
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