# A Novel Hybrid Model for Cantonese Rumor Detection on Twitter
## 2 Related Work  
Currently, most rumor detection methods are supervised. The literature related to rumor detection has been reviewed by several comprehensive surveys [8; 9; 10]. In our work, we briefly review some significant works based on deep learning methods. Deep learning has been the most revolutionary development in artificial intelligence, which is widely used in the field of rumor detection. The two most popular deep learning models are Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). For identifying rumors, [PERSON] et al. [4] utilized RNN to learn continuous representations of microblog events, which captured the variation of contextual information of relevant posts over time. Then, in [11], the authors improved this approach by combining the attention mechanism with RNN to focus on textual features with different attentions. Recently, [PERSON] et al. [12] employed the adversarial learning method to improve the performance of the rumor classifier, where the discriminator was used as a classifier and the corresponding generator improved the discriminator by generating conflicting noises. In addition, a multi-task learning approach was proposed by [PERSON] et al. [13] to solve the problem of rumor classification. To be specific, they implemented a multi-task learning framework with an LSTM layer shared among all tasks, as well as a number of task-specific layers. In [14], [PERSON] et al. designed a Tree LSTM model for rumor and stance detection that converted the propagation tree into a binarized constituency tree structure. The model applied convolution units in Tree LSTMs, which were better at learning patterns in features and employed the multi-task learning to propagate the useful stance signal up in the tree at the root node. But RNN was not qualified for the early detection of misinformation and had a bias towards the latest input elements. So in [15], [PERSON] et al. proposed a method based on CNN to learn key features scattered among an input sequence and shape high-level interactions among significant features. What's more, [PERSON] et al. [16] introduced a Two-Level CNN with User Response Generator (TCNN-URG) where TCNN captured underlying semantic information at word and sentence levels, and URG generateduser responses to new articles with the assistance of historical user responses. Furthermore, a mixture of RNNs and CNNs was exploited in recent works. [PERSON] et al. [6] incorporated both RNN and CNN to get the user features based on time series. In addition, a model of Credible Early Detection (CED) was presented by [PERSON] et al. [17] to detect rumors on social media based on repost information. CNN was leveraged to obtain feature vectors of original microblogs and repost sequence. Then, the repost sequence was sent into RNN. What's more, in [18], the authors proposed a multi-modal network comprising CNN and Long Short-Term Memory (LSTM) with the attention mechanism. It jointly learned representations of textual contents and social contexts in rumors. However, these methods were inefficient to learn the features of the propagation structure, and they ignored the global structural features of rumor dispersion. So, to focus on the differences between the characteristics in propagation of real and false information, [PERSON] et al. [7] proposed models based on top-down and bottom-up tree-structured Recursive Neural Networks (RvNN), which deeply integrated the structural and textual features of tweets for detecting rumors at early stages from propagation trees or networks. Based on this, they designed discriminative attention mechanisms for the RvNN-based models to selectively attend on the subset of evidential posts during the bottom-up/top-down recursive composition [19]. Moreover, in [20], the authors built a model based on Bi-Directional Graph Convolutional Network (Bi-GCN) to explore characteristics by operating on both top-down and bottom-up propagation of rumors. But these methods only detected rumors based on the meaning of text and ignored the sentiment of it.