The rapid dissemination of fake news through online news platforms and social media has become a major challenge, significantly influencing public opinion, political decision-making, and social stability. Recent fake news detection methods have achieved promising performance using deep contextual language models such as Bidirectional Encoder Representations from Transformers (BERT). However, most existing approaches process news articles independently and primarily focus on textual semantics while overlooking structural relationships among related news articles. Consequently, valuable graph-based information, including semantic similarity, shared entities, common publishers, and topical relationships, remains underutilized. This paper proposes GraphBERT4FakeNews, a graph transformer framework that jointly learns contextual semantic representations and graph structural information for English fake news detection. First, BERT generates contextualized embeddings from news articles. A news graph is then constructed by connecting semantically related articles according to contextual similarity. Subsequently, a GraphBERT encoder performs graph-aware self-attention to capture both local and long-range structural dependencies without relying on conventional graph convolution operations. Finally, a classification layer predicts whether a news article is genuine or fake. The proposed framework is evaluated on the publicly available FakeNewsNet benchmark, including the GossipCop and PolitiFact datasets. Experimental results demonstrate that GraphBERT4FakeNews effectively integrates semantic understanding with graph structural reasoning, leading to improved fake news detection performance compared with conventional language models and graph neural network approaches.

Graph convolutional networks have obviously influenced recommender systems. However, we argue that previous research has limitations: 1) unidirectional approaches are not enough to explicitly capture the representation learning, 2) social influence is ignored to extract the user preference drifts, and 3) simple and crude fusion methods are not enough to seamlessly amalgamate the diverse behavioral perspective. To address these limitations, this research proposes a novelty of Bidirectional Graph convolutional networks for social and sequential recommendation (BiG). A novel graph convolutional networks are modified into bidirectional graphs to explicitly learn the representation of dynamic user preference at social level and sequential level. A novel Bidirectional Gate (BiGate) is designed to amalgamate the diverse preferences of social and sequential influences. Empirical experiment illustrates that BiG outperforms state-of-the-art methods.