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Meanwhile, considering the domain attributes of news, the model is trained to extract inter-domain invariant features through Adversarial Neural Network ideation, and intra-domain knowledge information is utilized through graph convolutional networks (GCN) to detect emergent news. Through an extensive number of experiments on Chinese and English datasets from two major social media platforms, Weibo and Twitter, it is demonstrated that the model proposed in this paper can accurately screen multimodal emergent news on social media with an average accuracy of 88.7%. The contribution of this study lies not only in the improvement of model performance but also in the proposal of a solution for the challenges posed by limited labels and multimodal breaking news. 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