Emotion-Aware Graph Neural Meta-Learning for Few-Shot Manipulation Detection in Dialogues

• Aarthy N,
• Velammal B L,

Vol. 20, No. 2, February 28, 2026
10.3837/tiis.2026.02.012, Download Paper (Free):

• Dialogue System
• Graph Convolutional Networks
• Meta-Learning
• Manipulation Detection

Abstract

Detecting subtle forms of manipulation in multi-turn conversations is essential for mitigating misinformation and improving the quality of online dialogue. This work introduces a novel end-to-end manipulation detection pipeline that integrates semantic, emotional, and structural reasoning. Utterance-level semantic embeddings from RoBERTa are combined with emotional cues from EmoBERTa and keyword–emotion rules to provide weak supervision for manipulation strategies such as guilt, coercion, flattery, and distraction. Each dialogue is modeled as a feature-enriched graph in which utterances function as nodes and conversational transitions define edges, enabling more expressive discourse representation. A Graph Convolutional Network optimized with Model-Agnostic Meta-Learning learns contextual manipulation cues and demonstrates strong generalization under limited annotated data. To further improve interpretability, the system incorporates rule-based keyword matching and SHAP-based feature attribution, offering clear and transparent explanations for model predictions. Experimental results show that the proposed approach achieves a Macro F1 score of 89.4% and an accuracy of 95.3%, highlighting its effectiveness, robustness, and interpretability for manipulation detection in dialogues.

Statistics

Cite this article