@inproceedings{wang-etal-2024-mentalmanip,
    title = "{M}ental{M}anip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations",
    author = "Wang, Yuxin  and
      Yang, Ivory  and
      Hassanpour, Saeed  and
      Vosoughi, Soroush",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.acl-long.206/",
    doi = "10.18653/v1/2024.acl-long.206",
    pages = "3747--3764",
    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 $\textsc{MentalManip}$, which consists of 4,000 annotated fictional 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 $\textsc{MentalManip}$ will stimulate further research, leading to progress in both understanding and mitigating the impact of mental manipulation in conversations."
}