Mental manipulation, a significant form of abuse in interpersonal
conversations, presents a challenge to identify due to its context-dependent
and often subtle nature. The detection of manipulative language is essential
for protecting potential victims, yet the field of Natural Language Processing
(NLP) currently faces a scarcity of resources and research on this topic. Our
study addresses this gap by introducing a new dataset, named ${\rm M{\small
ental}M{\small anip}}$, which consists of $4,000$ annotated movie dialogues.
This dataset enables a comprehensive analysis of mental manipulation,
pinpointing both the techniques utilized for manipulation and the
vulnerabilities targeted in victims. Our research further explores the
effectiveness of leading-edge models in recognizing manipulative dialogue and
its components through a series of experiments with various configurations. The
results demonstrate that these models inadequately identify and categorize
manipulative content. Attempts to improve their performance by fine-tuning with
existing datasets on mental health and toxicity have not overcome these
limitations. We anticipate that ${\rm M{\small ental}M{\small anip}}$ will
stimulate further research, leading to progress in both understanding and
mitigating the impact of mental manipulation in conversations.

心理操纵是人际对话中一种具有挑战性的滥用形式，检测操纵性语言对保护潜在受害者至关重要；然而，自然语言处理领域目前在这一主题上面临资源和研究的匮乏。本研究通过引入名为 MentalManip 的新数据集，包含 4,000 个已标注的电影对话，填补了这一空白。该数据集可以对心理操纵进行全面分析，确定操纵所使用的技巧以及针对受害者的弱点。我们的研究进一步通过一系列实验，探索先进模型在识别操纵性对话及其组成部分方面的效果。结果表明，这些模型不能充分识别和分类操纵性内容。尝试通过对心理健康和有害性现有数据集进行微调以提高性能并未克服这些限制。我们预计 MentalManip 将推动进一步的研究，促进对对话中心理操纵的理解和减轻其影响的进展。