The rapid growth of short video platforms has made multimodal fake news more prevalent. Existing detectors suffer from two major limitations: (I) global-alignment bias that overemphasizes holistic cross-modal matching and thus misses subtle, localized inconsistencies; and (II) LLM-based methods that leverage powerful generative reasoning to identify cognitive forgeries but inherently suffer from hallucinations and high inference latency. To overcome these limitations, we propose PCDD, a novel Perception-Cognition Dual-driven Detector that jointly observes the form and probes the logic for short video fake news detection. The perception stream exposes fine-grained cross-modal conflicts by amplifying localized inconsistencies into explicit discrepancies. The cognition stream transfers reasoning capabilities from LLMs to a lightweight student to mine cognitive forgeries, while reducing the risk of hallucinations and eliminating reliance on LLMs at inference. Experiments on real-world datasets show that PCDD consistently outperforms baselines, while improving interpretability and robustness in data scarcity scenarios. Our code is available at: https://github.com/SeinCore/PCDD.

Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify emotion utterances and their corresponding cause utterances in unannotated conversations, this task that has garnered increasing attention recently. Previous methods often apply Emotion-Cause Pair Extraction (ECPE) task models, treating the entire conversation as a whole for contextual interaction. However, statistical analysis shows that the number of emotion-cause pairs in ECPEC conversation data far exceeds that in ECPE datasets, leading to interference among multiple events within a conversation and causing noise to propagate between different events. To address this issue, we propose a novel CEnter eveNT-guided framEwoRk (CENTER). This model introduces a Center Event Detection task to construct a center event-aware graph that captures the unique representations of different event regions. Additionally, mimicking human reasoning processes, we build a center event reasoning graph and use graph neural network to facilitate the flow of information between utterance pairs, thereby uncovering the relationships between emotions and their causes. Experimental results demonstrate that our approach achieves state-of-the-art performance across three benchmark datasets.