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

arXiv: https://arxiv.org/abs/2405.16584 · HTML: https://mlsanity.com/paper/2405.16584/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 26 May 2024.

## Builds on (most cited first, 15 of 15)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://mlsanity.com/paper/2306.11698/) (2023)
- [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://mlsanity.com/paper/2203.09509/) (2022)
- [Dreaddit: A Reddit Dataset for Stress Analysis in Social Media](https://mlsanity.com/paper/1911.00133/) (2019)
- [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017)
- [Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts](https://mlsanity.com/paper/2108.11830/) (2021)
- [Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction](https://mlsanity.com/paper/2102.09427/) (2021)
- [Tackling Online Abuse: A Survey of Automated Abuse Detection Methods](https://mlsanity.com/paper/1908.06024/) (2019)
- [TalkDown: A Corpus for Condescension Detection in Context](https://mlsanity.com/paper/1909.11272/) (2019)
- [Understanding and Measuring Psychological Stress using Social Media](https://mlsanity.com/paper/1811.07430/) (2018)
- [Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements](https://mlsanity.com/paper/2302.09270/) (2023)
- [Detection of Cyberbullying Incidents on the Instagram Social Network](https://mlsanity.com/paper/1503.03909/) (2015)
- [Hidden behind the obvious: misleading keywords and implicitly abusive language on social media](https://mlsanity.com/paper/2205.01374/) (2022)

## Built on by (most cited first, 21 of 21)

- [Human Decision-making is Susceptible to AI-driven Manipulation](https://mlsanity.com/paper/2502.07663/) (2025)
- [Detecting Conversational Mental Manipulation with Intent-Aware Prompting](https://mlsanity.com/paper/2412.08414/) (2024)
- [Enhanced Detection of Conversational Mental Manipulation Through Advanced Prompting Techniques](https://mlsanity.com/paper/2408.07676/) (2024)
- [ProMind-LLM: Proactive Mental Health Care via Causal Reasoning with Sensor Data](https://mlsanity.com/paper/2505.14038/) (2025)
- [Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication](https://mlsanity.com/paper/2502.08896/) (2025)
- [ChatbotManip: A Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour](https://mlsanity.com/paper/2506.12090/) (2025)
- [Probing Association Biases in LLM Moderation Over-Sensitivity](https://mlsanity.com/paper/2505.23914/) (2025)
- [Boosting Large Language Models for Mental Manipulation Detection via Data Augmentation and Distillation](https://mlsanity.com/paper/2505.15255/) (2025)
- [SELF-PERCEPT: Introspection Improves Large Language Models' Detection of Multi-Person Mental Manipulation in Conversations](https://mlsanity.com/paper/2505.20679/) (2025)
- [Unraveling SITT: Social Influence Technique Taxonomy and Detection with LLMs](https://mlsanity.com/paper/2506.00061/) (2025)
- [The Hidden Puppet Master: Predicting Human Belief Change in Manipulative LLM Dialogues](https://mlsanity.com/paper/2603.20907/) (2026)
- [CLAIM: An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues](https://mlsanity.com/paper/2506.04131/) (2025)
- [Detecting Mental Manipulation in Speech via Synthetic Multi-Speaker Dialogue](https://mlsanity.com/paper/2601.08342/) (2026)
- [EchoGuard: An Agentic Framework with Knowledge-Graph Memory for Detecting Manipulative Communication in Longitudinal Dialogue](https://mlsanity.com/paper/2603.04815/) (2026)
- [Vishing-Tactics-Bench: Forecasting Exploitation Trajectories in Voice Phishing Calls](https://mlsanity.com/paper/2609.07151/) (2026)
- [Advancing Uto-Aztecan Language Technologies: A Case Study on the Endangered Comanche Language](https://mlsanity.com/paper/2505.18159/) (2025)
- [Explainable Detection of Implicit Influential Patterns in Conversations via Data Augmentation](https://mlsanity.com/paper/2506.14211/) (2025)
- [Train Yourself as an LLM: Exploring Effects of AI Literacy on Persuasion via Role-playing LLM Training](https://mlsanity.com/paper/2604.02637/) (2026)
- [LLM Wardens: Mitigating Adversarial Persuasion with Third-Party Conversational Oversight](https://mlsanity.com/paper/2605.08321/) (2026)
- [IMPACTeen: Intentions, Manipulation, Persuasion, Annotations, and Consequences in Teen Communication Dataset](https://mlsanity.com/paper/2606.16910/) (2026)
- [ConVAWG: A Retrieval-Grounded Framework for Controlled Synthetic Dialogue Generation in Violence Against Women and Girls](https://mlsanity.com/paper/2608.11200/) (2026)

## Abstract (arXiv)

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 Msmall entalMsmall 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 Msmall entalMsmall anip will stimulate further research, leading to progress in both understanding and mitigating the impact of mental manipulation in conversations.