Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
134 tokens/sec
GPT-4o
10 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversations (2405.16584v1)

Published 26 May 2024 in cs.CL

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 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.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. Just say no: Analyzing the stance of neural dialogue generation in offensive contexts. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4846–4862. Association for Computational Linguistics.
  2. Anne Barnhill. 2014. What is manipulation? In Manipulation: Theory and Practice. Oxford University Press.
  3. Anne Barnhill. 2022. How philosophy might contribute to the practical ethics of online manipulation. In The philosophy of online manipulation, pages 49–71. Routledge.
  4. SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in Twitter. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 54–63, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
  5. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712.
  6. Natural language processing of social media as screening for suicide risk. Biomedical Informatics Insights, 10:1178222618792860.
  7. Cristian Danescu-Niculescu-Mizil and Lillian Lee. 2011. Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs. In Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics, pages 76–87, Portland, Oregon, USA. Association for Computational Linguistics.
  8. Discovering shifts to suicidal ideation from mental health content in social media. In Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference, volume 2016, pages 2098–2110.
  9. Towards safer generative language models: A survey on safety risks, evaluations, and improvements.
  10. Measuring and mitigating unintended bias in text classification. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, pages 67–73, New Orleans LA USA. ACM.
  11. Facebook language predicts depression in medical records. In Proceedings of the National Academy of Sciences, volume 115, pages 11203–11208. Proceedings of the National Academy of Sciences.
  12. Joseph L. Fleiss and Jacob Cohen. 1973. The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educational and Psychological Measurement, 33:613–619.
  13. Lei Gao and Ruihong Huang. 2017. Detecting online hate speech using context aware models. In Proceedings of the International Conference Recent Advances in Natural Language Processing, pages 260–266. Incoma Ltd. Shoumen, Bulgaria.
  14. Understanding and measuring psychological stress using social media. In Proceedings of the International AAAI Conference on Web and Social Media, volume 13, pages 214–225.
  15. The consequences of psychological abuse and control in intimate partner relationships. Traumatology.
  16. Deep learning for suicide and depression identification with unsupervised label correction. In Artificial Neural Networks and Machine Learning, pages 436–447. Springer International Publishing.
  17. ToxiGen: A large-scale machine-generated dataset for adversarial and implicit hate speech detection. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, pages 3309–3326, Dublin, Ireland. Association for Computational Linguistics.
  18. Detection of cyberbullying incidents on the instagram social network. arXiv preprint arXiv:1503.03909.
  19. Marcello Ienca. 2023. On artificial intelligence and manipulation. Topoi, 42(3):833–842.
  20. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
  21. Detecting troll tweets in a bilingual corpus. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6247–6254, Marseille, France. European Language Resources Association.
  22. Tackling online abuse: A survey of automated abuse detection methods. arXiv preprint arXiv:1908.06024.
  23. Early Identification of Depression Severity Levels on Reddit Using Ordinal Classification. In Proceedings of the ACM Web Conference 2022, WWW ’22, pages 2563–2572, New York, NY, USA. Association for Computing Machinery.
  24. Stress detection using natural language processing and machine learning over social interactions. Journal of Big Data, 9(1):33.
  25. Using fuzzy fingerprints for cyberbullying detection in social networks. In 2018 IEEE International Conference on Fuzzy Systems, pages 1–7, Rio de Janeiro, Brazil. IEEE Press.
  26. George K Simon and Kevin Foley. 2011. In sheep’s clothing: Understanding and dealing with manipulative people. Tantor Media, Incorporated.
  27. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
  28. Elsbeth Turcan and Kathy McKeown. 2019. Dreaddit: A reddit dataset for stress analysis in social media. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis, pages 97–107, Hong Kong. Association for Computational Linguistics.
  29. Decodingtrust: A comprehensive assessment of trustworthiness in GPT models. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track.
  30. Zijian Wang and Christopher Potts. 2019. Talkdown: A corpus for condescension detection in context. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 3709–3717, Hong Kong, China. Association for Computational Linguistics.
  31. Zeerak Waseem. 2016. Are you a racist or am i seeing things? annotator influence on hate speech detection on twitter. In Proceedings of the First Workshop on NLP and Computational Social Science, pages 138–142, Austin, Texas. Association for Computational Linguistics.
  32. Implicitly abusive language – what does it actually look like and why are we not getting there? In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 576–587, Online. Association for Computational Linguistics.
  33. Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students. In Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, volume 3, pages 116:1–116:33.
  34. Mental-llm: Leveraging large language models for mental health prediction via online text data. arXiv preprint arXiv:2307.14385.
  35. Towards interpretable mental health analysis with large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6056–6077, Singapore. Association for Computational Linguistics.
  36. DeTexD: A Benchmark Dataset for Delicate Text Detection. In The 7th Workshop on Online Abuse and Harms (WOAH), pages 14–28, Toronto, Canada. Association for Computational Linguistics.
  37. Wenjie Yin and Arkaitz Zubiaga. 2022. Hidden behind the obvious: Misleading keywords and implicitly abusive language on social media. Online Social Networks and Media, 30:100210.
  38. A taxonomy, data set, and benchmark for detecting and classifying malevolent dialogue responses. Journal of the Association for Information Science and Technology, 72(12):1477–1497.
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets