MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations
<|reference_start|>MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations: 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.<|reference_end|>
@article{wang2024mentalmanip:,
    title={MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation
  in Conversations},
    author={Yuxin Wang, Ivory Yang, Saeed Hassanpour, Soroush Vosoughi},
    journal={arXiv preprint arXiv:2405.16584},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16584},
    primaryClass={cs.CL}
}