GraphBERT4FakeNews: Graph-aware Bidirectional Transformer Learning for English Fake News Detection
จำนวนผู้เข้าชม
48
Creative Commons License
ยังไม่มีไฟล์เอกสาร
| Collection | RMUTK Research Repository (RMUTK IR) บทความประชุมวิชาการ — Proceedings |
| ID RMUTK Digital | RMUTK000024 |
| Title | GraphBERT4FakeNews: Graph-aware Bidirectional Transformer Learning for English Fake News Detection |
| Alternative title | GraphBERT4FakeNews: Graph-aware Bidirectional Transformer Learning for English Fake News Detection |
| Authors | ผศ.ดร.นิกร กรรณิกากลาง Corresponding นางสาววาสนา ด้วงเหมือน ผู้แต่งหลัก อ.รุ่งทิพย์ โคบาล ผศ.สุรเทพ แป้นเกิด |
| Faculty | คณะบริหารธุรกิจ |
| Volume / Issue / Pages | pp.- |
| Conference | 12th International Conference on Creative Technology (CreTech2026) |
| Conference Date | 2026-08-08 |
| Conference Location | โรงแรมแกรนด์ พาลาสโซ่ พัทยา จังหวัดชลบุรี |
| Year | 2569 |
| Level | ระดับนานาชาติ |
| DOI | - |
| ISBN / ISSN | - |
| Funding Source | มทร.กรุงเทพ |
| Abstract | 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. |
| Abstract (EN) | 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. |
| Keywords (TH) | Fake News DetectionGraphBERTBERTGraph Neural NetworkGraph TransformerFakeNewsNet |
| Keywords (EN) | Fake News DetectionGraphBERTBERTGraph Neural NetworkGraph TransformerFakeNewsNet |
| Access Level | Open Access (เปิดสาธารณะ) |
เอกสารดิจิทัล
ยังไม่มีไฟล์เอกสาร