Dynamic Emotion and Personality Profiling for Multimodal Deception Detection

• URL: http://arxiv.org/abs/2604.17037v1
• Date: Sat, 18 Apr 2026 15:38:36 GMT
• Title: Dynamic Emotion and Personality Profiling for Multimodal Deception Detection
• Abstract summary: Experimental results show that our Rel-DDEP significantly outperforms the existing state-of-the-art baseline models in three tasks.<n>The F1 score of the deception detection increases by 2.53%, that of the emotion detection increases by 2.66%, and that of the personality detection increases by 9.30%.
• Score: 35.29389806267121
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deception detection is of great significance for ensuring information security and conducting public opinion analysis, with personality factors and emotion cues playing a critical role. However, existing methods lack sample-level dynamic annotations for emotions and personality.In this paper, we propose an innovative multi-model multi-prompt annotation scheme and a strict label quality evaluation standard, and establish a multimodal joint detection dataset DDEP for deception, emotion, and personality. Meanwhile, we propose Rel-DDEP, an adaptive reliability-weighted fusion framework. Our framework quantifies uncertainty by mapping modal features to a high-dimensional Gaussian distribution space. It then performs reliability-weighted fusion and incorporates an alignment module and a sorting constraint module to achieve joint detection of deception, emotion, and personality. Experimental results on the MDPE and DDEP datasets show that our Rel-DDEP significantly outperforms the existing state-of-the-art baseline models in three tasks. The F1 score of the deception detection increases by 2.53%, that of the emotion detection increases by 2.66%, and that of the personality detection increases by 9.30%. The experiments fully verify the necessity of annotating dynamic emotion and personality labels for each sample and the effectiveness of reliability-weighted fusion.

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