  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.
