首次用实证数据检测土耳其新闻中AI生成内容，发现约2.5%文章被大模型改写。
From Perceptions To Evidence: Detecting AI-Generated Content In Turkish News Media With A Fine-Tuned Bert Classifier
- 用土耳其语BERT模型对3600篇新闻微调，二分类识别AI重写内容。
- 测试集F1达0.9708，平均置信度超0.96，估计2.5%新闻被大模型修改。
- 突破仅靠记者自述的局限，为中文读者提供可参考的检测方法。
大型语言模型快速融入新闻编辑流程，引发对在线媒体中AI生成内容普遍性的关注。尽管已有研究在英语媒体中量化该现象，但针对土耳其新闻媒体的实证调查仍属空白，现有研究多局限于记者的定性访谈或假新闻检测。本研究通过在三个具有不同编辑立场的主流土耳其媒体共3600篇文章上微调土耳其语BERT模型（dbmdz/bert-base-turkish-cased），实现对AI重写内容的二分类。模型在保留测试集上取得0.9708的F1分数，两类样本的精确率与召回率对称。随后在超过3500篇未见文章（2023–2026年）上部署，结果呈现跨媒体与时间稳定的分类模式，平均预测置信度超过0.96，估算平均每2.5%的新闻内容被大模型重写或修订。据我们所知，这是首个从记者感知转向数据驱动、实证测量土耳其新闻中大模型使用的研究所。
原文摘要 · Abstract (English)
The rapid integration of large language models into newsroom workflows has raised urgent questions about the prevalence of AI-generated content in online media. While computational studies have begun to quantify this phenomenon in English-language outlets, no empirical investigation exists for Turkish news media, where existing research remains limited to qualitative interviews with journalists or fake news detection. This study addresses that gap by fine-tuning a Turkish-specific BERT model (dbmdz/bert-base-turkish-cased) on a labeled dataset of 3,600 articles from three major Turkish outlets with distinct editorial orientations for binary classification of AI-rewritten content. The model achieves 0.9708 F1 score on the held-out test set with symmetric precision and recall across both classes. Subsequent deployment on over 3,500 unseen articles spanning between 2023 and 2026 reveals consistent cross-source and temporally stable classification patterns, with mean prediction confidence exceeding 0.96 and an estimated 2.5 percentage of examined news content rewritten or revised by LLMs on average. To the best of our knowledge, this is the first study to move beyond self-reported journalist perceptions toward empirical, data-driven measurement of AI usage in Turkish news media.
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