# Research on Dual-Emotion Feature Fusion and Performance Improvement in Rumor Detection
## 2 Related Work
### Starting with Feature Analysis  
[PERSON] et al. [18] proposed a novel graph convolution network model named the multilevel feature fusion-based graph convolution network (MFF-GCN), which employs multiple streams of GCNs that each learn different-level features of rumor data. In [19], a multi-feature fusion neural network with an attention mechanism was proposed for rumor detection, which makes an attempt to integrate user, textual, and propagation features into one united framework. Specifically, a Bidirectional Long Short-Term Memory Network (Bi-LSTM) is applied to extract user and textual features, and a Graph Convolutional Neural Network (GCN) is employed to extract high-order propagation features. [PERSON] et al. verified the relationship between the authenticity of news (rumor or non-rumor) and the use of sentimental words and designed the emotional features (the ratio of the number of negative to positive words) [20]. [PERSON] et al. did not add a single feature but extracted richer emotional features from news content for rumor detection based on emotional vocabulary [21]. [PERSON] et al. proposed a new method that used a conditional random field (CRF) to learn from the sequence dynamics of social media posts by using content features and social features [22]. The results showed that the F1 score was 60.7%, with low accuracy. [PERSON] et al. explored the impact of emotional characteristics on the detection of different types of misinformation (i.e., propaganda, hoax, clickbait, and sarcasm) [23]. In 2020, [PERSON] and [PERSON] proposed an adaptive fusion network for rumor detection that models affective embeddings from content and comments, but it does not explore the specific differences in dual affective signals between rumors and non-rumors [24]. [PERSON] et al. [25] paid attention to the fact that the emotions (i.e., social emotions) aroused by news comments in the population cannot be ignored and verified the difference between dual emotions in fake news and real news. [PERSON] et al. [26] extracted emotional and psycholinguistic features from posts and comments to enhance their ap  
Figure 2: Common rumor detection models. proach and make rumor detection more effective. [PERSON] et al. [27] used recent advances in natural language processing (NLP) to introduce a context-aware rumor detection method for Arabic social media. It can be seen that many experiments by scholars have proved that methods for rumor detection still have infinite possibilities, and how to give full play to the role of emotional signals needs to be further studied and explored. The current research on emotion analysis is developing in a more in-depth and comprehensive direction by not only focusing on emotion words and sentimental words in the text but also combining content features and social features, using big data and machine learning technology to build emotional models, and exploring the relationship between rumors and emotional features. These studies not only improve the accuracy and efficiency of sentiment analysis but also provide powerful analytical tools for psychology, sociology, marketing, and other fields. The analysis of emotional characteristics is shown in Figure 3.  
In order to visually show the rumor detection methods mentioned in different references, this paper compares the characteristics of different methods in Table 1.