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arXiv · 2405.16584

MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations

Yuxin Wang·Ivory Yang·Saeed Hassanpour·Soroush Vosoughi
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Abstract

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.

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Yuxin Wang, Ivory Yang, Saeed Hassanpour, Soroush Vosoughi. 2024-05-26. MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations. https://arxiv.org/abs/2405.16584

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