用大模型生成虚假网络言论，测试自动识别的可行性。
SLURG: Investigating the Feasibility of Generating Synthetic Online Fallacious Discourse
- 用大模型生成论坛风格的虚假论断，模拟真实网络语境。
- 高质提示词让模型更好模仿论坛词汇多样性，语法接近真实数据。
- 适合研究虚假信息检测或安全评估的团队参考。
本文探讨了逻辑谬误在社交媒体操纵行为中的定义与扩展，聚焦于互联网论坛中真实存在的谬误现象，尤其关注围绕俄乌冲突讨论区中的误导性言论。尽管自动谬误检测近年受关注，但现有数据集多采用非规范分类体系，或局限于政治辩论、新闻报道等正式语言场景，难以覆盖网络话语中非标准化、多样化的表达。为此，我们提出Shady Linguistic Utterance Replication-Generation（SLURG）框架，利用大型语言模型DeepHermes-3-Mistral-24B生成合成的虚假论坛评论，验证其可行性。实验表明，该模型能有效复现真实数据的句法模式，且高质量少样本提示显著提升其对在线论坛词汇多样性的模仿能力。
原文摘要 · Abstract (English)
In our paper we explore the definition, and extrapolation of fallacies as they pertain to the automatic detection of manipulation on social media. In particular we explore how these logical fallacies might appear in the real world i.e internet forums. We discovered a prevalence of misinformation / misguided intention in discussion boards specifically centered around the Ukrainian Russian Conflict which serves to narrow the domain of our task. Although automatic fallacy detection has gained attention recently, most datasets use unregulated fallacy taxonomies or are limited to formal linguistic domains like political debates or news reports. Online discourse, however, often features non-standardized and diverse language not captured in these domains. We present Shady Linguistic Utterance Replication-Generation (SLURG) to address these limitations, exploring the feasibility of generating synthetic fallacious forum-style comments using large language models (LLMs), specifically DeepHermes-3-Mistral-24B. Our findings indicate that LLMs can replicate the syntactic patterns of real data} and that high-quality few-shot prompts enhance LLMs' ability to mimic the vocabulary diversity of online forums.
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