## 2 Related Work
### Rumor Detection  
The rumor detection task is similar to many tasks, such as spam detection [11] and satirical article detection [12]. Based on the related work of previous researchers, we define false information that can be officially confirmed as rumors. Early research focused on effective feature extraction from manual settings, including the linguistic features of the message itself, and the contextual features of the communicative process. [PERSON] et al. [13] used statistics on textual information such as special characters, number of links, etc., to detect rumors. [PERSON] et al. [14] studied features such as the subject of tweets and used Bayesian networks as classifiers to identify rumors. [PERSON] et al. [15] found that disinformation releases had more significant periodic fluctuations and used random forests to fit time-series features. [PERSON] et al. [1] introduced propagation trees into rumor detection models and used a kernelized random walk algorithm to construct classifiers. These research methods were mainly based on manual feature extraction for modeling, which required complex feature engineering. With the development of deep learning, based on the above-mentioned, some scholars started to explore rumor detection using deep learning as a way to offset the drawbacks of using manual approaches for feature extraction. For example, in terms of content and propagation structure based on rumor texts, [PERSON] et al. [16] analyzed the syntax and semantics in text using a model based on recurrent neural networks (RNNs) with complex recurrent units and additional hidden layers and features that could be used to learn hidden representations and capture contextual information about relevant posts over time, and they used the captured contextual information for rumor detection. [PERSON] et al. [17] proposed a deep attention model that leveraged recurrent neural networks (RNNs) to selectively learn the temporal hidden representations of sequential posts, with the aim of identifying rumors. In their paper, the authors undertook a comprehensive study of the recursive nature of RNNs, analyzing features with specific weights to capture hidden feature representations of rumors and contextual changes in related posts over time. [PERSON] et al. [18] proposed a convolutional approach for misinformation identification (CAMI), which leveraged a convolutional neural network (CNN) for the purpose of detecting rumor information. CAMI was able to extract pivotal features that were dispersed throughout an input sequence and to shape elevated-level interactions among significant features. This enabled the effective identification of rumor and facilitated the timely detection of misinformation in practice. In terms of rumor propagation structure, [PERSON] et al. [19] also proposed a novel neural network architecture that adopted both bottom-up and top-down tree structures. That architecture was designed to effectively extract discriminative feature content from tweets via a discontinuous propagation structure and subsequently generate multiple feature representations to enable an accurate identification and classification of rumors. [PERSON] et al. [20] proposed a model that could automatically determine the credibility of news based on the unknown attributes of fake news and the connection between articles, authors, and news topics. The model extracted explicit and implicit features in the text and identified rumors by building a deep spreading network. [PERSON] et al. [21] built a time-series classifier to capture the content of tweets, the connection between author and topic, and the change of user features in the propagation path, respectively. Currently, media news on social networks usually consists of text, images, or videos, and it has been proved that rich visual representation in media news can also provide effective information [22]. Among the text-image-based rumor detection methods, [PERSON] et al. [2] proposed a novel recurrent neural network with an attention mechanism (att-RNN).