用增强数据提升模型识别对话中隐性操控模式的能力
Explainable Detection of Implicit Influential Patterns in Conversations via Data Augmentation
- 利用大模型推理能力扩充数据，增强对隐性操控模式的检测
- 检测准确率提升6%，影响手段与受害者脆弱性识别分别提高33%、43%
- 可定位对话中具体被操控位置，适合舆情分析与安全研究者
在数字化时代，人们越来越依赖数字平台进行交流和获取新闻，各类主体通过语言策略影响公众认知。尽管模型已能有效识别显性模式（如社交媒体评论），但恶意行为者正转向使用嵌入于对话中的隐性影响力语言模式，以潜移默化方式影响目标心理，从而实现非直接的信息获取。本文提出一种改进方法，用于检测此类隐性影响力模式，并能精确定位其在对话中的具体位置。通过利用先进语言模型的推理能力对现有数据集进行增强，所设计框架使隐性影响力模式检测准确率提升6%；同时，在多标签分类任务中，对影响手段和受害者脆弱性的识别分别提升33%和43%。
原文摘要 · Abstract (English)
In the era of digitalization, as individuals increasingly rely on digital platforms for communication and news consumption, various actors employ linguistic strategies to influence public perception. While models have become proficient at detecting explicit patterns, which typically appear in texts as single remarks referred to as utterances, such as social media posts, malicious actors have shifted toward utilizing implicit influential verbal patterns embedded within conversations. These verbal patterns aim to mentally penetrate the victim's mind in order to influence them, enabling the actor to obtain the desired information through implicit means. This paper presents an improved approach for detecting such implicit influential patterns. Furthermore, the proposed model is capable of identifying the specific locations of these influential elements within a conversation. To achieve this, the existing dataset was augmented using the reasoning capabilities of state-of-the-art language models. Our designed framework resulted in a 6% improvement in the detection of implicit influential patterns in conversations. Moreover, this approach improved the multi-label classification tasks related to both the techniques used for influence and the vulnerability of victims by 33% and 43%, respectively.
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