Resumen
The detection of fake news has emerged as a crucial area of research due to its potential impact on society. In this study, we propose a robust methodology for identifying fake news by leveraging diverse aspects of language representation and incorporating auxiliary information. Our approach is based on the utilisation of Bidirectional Encoder Representations from Transformers (BERT) to capture contextualised semantic knowledge. Additionally, we employ a multichannel Convolutional Neural Network (mCNN) integrated with stacked Bidirectional Gated Recurrent Units (sBiGRU) to jointly learn multi-aspect language representations. This enables our model to effectively identify valuable clues from news content while simultaneously incorporating content- and context-based cues, such as user posting behaviour, to enhance the detection of fake news. Through extensive experimentation on four widely used real-world datasets, our proposed framework demonstrates superior performance (?3.59% (PolitiFact), ?6.8% (GossipCop), ?2.96% (FA-KES), and ?12.51% (LIAR), considering both content-based features and additional auxiliary information) compared to existing state-of-the-art approaches, establishing its effectiveness in the challenging task of fake news detection.