IConMoE: Modeling Intents of Misinformation using Concept Activation Vector-based Mixture of Experts

Understanding the intent behind misinformation helps reveal distinct patterns and underlying motives, enabling more accurate and efficient detection. However, existing approaches often struggle to effectively model the interplay of multiple coexisting intents within claims, and typically require additional LLM inferences to infer intent for each sample. In this paper, we introduce an intent-informed concept activation vector-based mixture-of-experts framework, iConMoE, which models latent intents in an unsupervised manner and predicts the veracity of claims in an end-to-end fashion, without the need for additional LLM inferences. Evaluated on three benchmark datasets, our method consistently outperforms both baseline and state-of-the-art models. We also conduct comprehensive ablation studies and error analyses to highlight the contributions of individual components and identify key challenges. In addition, we provide a qualitative analysis of the learned intents across samples to offer deeper insights into the model's reasoning.