# Research on Dual-Emotion Feature Fusion and Performance Improvement in Rumor Detection
## 2 Related Work
### Starting with Model Selection  
In recent years, scholars have been exploring rumor detection models. One of the first machine learning-based models to emerge was one that learned to recognize rumors from training data. Such models can automatically learn new rumor forms and detect them without human intervention. Their advantages are strong adaptability and good scalability, but the disadvantage is that a large amount of data are required to train the model, and the accuracy may be affected by the quality of the training data. Such models include naive Bayes classifiers, decision trees, support vector machines (SVMs), and so on. With the development of technology, multimodal rumor detection models have attracted more and more attention. This kind of model not only considers text information but also integrates multiple modes of information, such as pictures, videos, and user behaviors, to detect rumors. [PERSON] et al. proposed joint detection using a Tanh-RNN model, LSTM, and a GRU model and made two datasets for evaluation [8]. [PERSON] et al. proposed a method for identifying error information based on a CNN model (CAMI) [9]. [PERSON] et al. proposed a BiLSTM model that combined the hierarchical network framework with manually established social features [10]. [PERSON] et al. combined CNN and RNN models, using LSTM to represent tweets, and provided training sets from online rumor collection websites, with an accuracy rate of 81.9% [11]. [PERSON] et al. adopted the RNN + attention mechanism model and integrated multimodal features, cross-combined the features of texts, pictures, and videos, and tested the method on datasets from Twitter and Weibo, respectively, with the highest probabilities of 77.8% and 68.2% [12]. [PERSON] et al. combined an LSTM-RNN model with a Word2 Vec model to compare the evaluation data before and after adding grammatical features and social features, but the accuracy was not significantly improved [13]. [PERSON] et al. used the embedded layer of a BiLSTM and CNN model for testing and evaluation, and the result was 86.12% [14]. [PERSON] et al. proposed a zero-shot response-aware prompt learning framework to bridge language and domain gaps in rumor detection. They presented a prompt-based approach to avoid reliance on language-specific rumor prompt engineering, with effective response fusion strategies to incorporate influential and structural propagation threads for domain adaptation [15]. For [PERSON] et al., their intuition was to exploit diverse counterfactual evidence in an event graph to serve as multi-view interpretations, which were further aggregated for robust rumor detection results [16]. The inconsistencies between modalities are vital, so [PERSON] et al. developed a novel deep visual-linguistic fusion network (DVLFN) that considers cross-modal inconsistencies and detects rumors by comprehensively considering modal aggregation and contrast information. The evolution of rumor detection models from traditional machine learning approaches like naive Bayes, decision trees, and SVMs to more sophisticated multimodal frameworks highlights the increasing complexity and sophistication of the field. The integration of multiple data modalities, including text, images, videos, and user behaviors, has significantly improved the accuracy and robustness of rumor detection systems. The studies mentioned above, such as [PERSON]'s joint detection model, [PERSON]'s CNN-based method, [PERSON]'s BiLSTM model, and [PERSON]'s RNN+Attention model, demonstrate the diverse strategies researchers have employed to tackle the challenge of rumor detection. However, despite these advancements, there remains a need for models that can effectively handle the intermodal inconsistencies and domain gaps that arise when fusing information from different sources. The works by [PERSON] et al.