The wide spread of fake news has caused huge losses to both governments and the public. Many existing works on fake news detection utilized spreading information like propagatorsâô profiles and the propagation structure. However, such methods face the difficulty of data collection and cannot detect fake news at the early stage. An alternative approach is to detect fake news solely based on its content. Early content-based methods rely on manually designed linguistic features. Such shallow features are domain-dependent, and cannot easily be generalized to cross-domain data. Recently, many natural language processing tasks resort to deep learning methods to learn word, sentence, and document representations. In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations. In SemSeq4FD, we model the global pair-wise semantic relations between sentences as a complete graph, and learn the global sentence representations via a graph convolutional network with self-attention mechanism. Considering the importance of local context in conveying the sentence meaning, we employ a 1D convolutional network to learn the local sentence representations. The two representations are combined to form the enhanced sentence representations. Then a LSTM-based network is used to model the sequence of enhanced sentence representations, yielding the final document representation for fake news detection. Experiments conducted on four real-world datasets in English and Chinese, including cross-source and cross-domain datasets, demonstrate that our model can outperform the state-of-the-art methods.
SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection