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

TL;DR

This paper introduces a new dataset, MentalManip, of 4,000 annotated dialogues to analyze mental manipulation in conversations, revealing current models' limitations in detecting subtle manipulative language.

Contribution

The creation of the MentalManip dataset provides a valuable resource for studying mental manipulation, and the analysis highlights the challenges faced by existing models in identifying manipulative content.

Findings

Models perform poorly in detecting manipulation.

Fine-tuning on related datasets does not improve detection.

The dataset enables detailed analysis of manipulation techniques.

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.