检测俄乌战争期间社交媒体中的操纵性叙事，提升信息辨识力。
Hidden Persuasion: Detecting Manipulative Narratives on Social Media During the 2022 Russian Invasion of Ukraine
- 用Gemma 2模型微调并结合元特征优化分类
- 通过XLM-RoBERTa实现多目标文本片段定位
- 在两项任务中分别获第二、第三名，效果领先
本文是应对2025年联合国语言处理挑战赛中关于社交媒体操纵检测任务的顶尖解决方案之一。该任务聚焦于识别并分类影响乌克兰Telegram用户的信息操纵策略。针对分类任务，我们基于Gemma 2语言模型，采用LoRA适配器进行微调，并引入二级分类器结合元特征与阈值优化；针对文本片段检测任务，使用XLM-RoBERTa模型进行多目标训练，实现令牌级二分类。最终方法在分类任务中位列第2，在跨度检测任务中位列第3。
原文摘要 · Abstract (English)
This paper presents one of the top-performing solutions to the UNLP 2025 Shared Task on Detecting Manipulation in Social Media. The task focuses on detecting and classifying rhetorical and stylistic manipulation techniques used to influence Ukrainian Telegram users. For the classification subtask, we fine-tuned the Gemma 2 language model with LoRA adapters and applied a second-level classifier leveraging meta-features and threshold optimization. For span detection, we employed an XLM-RoBERTa model trained for multi-target, including token binary classification. Our approach achieved 2nd place in classification and 3rd place in span detection.
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