Textual Persuasion Analysis for Identifying Manipulative Languages

Author(s)	Bhavana R K, Sandeep albert Mathias
Country	India
Abstract	Manipulative language, often used in political, commercial, and social discourse, can distort public opinion by appealing to emotions, authority, or fallacious reasoning. This paper presents a comprehensive framework for detecting such manipulation using natural language processing. Leveraging a taxonomy of 25 persuasion techniques, we fine-tune the XLM-RoBERTa transformer model to perform multi-label classification of manipulative content. Additionally, we develop a counter-narrative generation module using a T5-based model to suggest ethically persuasive responses. Our results show promising accuracy in classification across varied techniques and demonstrate the effectiveness of automated counter-speech generation. This work contributes to efforts in enhancing digital media literacy and resisting propaganda in online platforms.
Keywords	Manipulative Language, Textual Persuasion Analysis, Natural Language Processing (NLP) XLM-RoBERTa, Multi-label Classification
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-13

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International Journal For Multidisciplinary Research