# Research on Dual-Emotion Feature Fusion and Performance Improvement in Rumor Detection
## 5 Conclusions and Future Work  
In this paper, we have established a model of dual-emotion signals for rumor detection, leveraging the unique characteristics derived from the similarity and difference between publisher emotions, social emotions, and dual emotions. Recognizing the limitations of single-emotion feature analysis, we have introduced semantic-feature-based BiGRU and CNN models for fine-grained text analysis. Through comparative experimental analysis on multiple datasets, we have demonstrated the feasibility and progress of our proposed models. The experimental results show that the accuracy of the BiGRU and CNN models on the Weibo-16 dataset reaches 87.9% and 88.3%, respectively, surpassing the DTC model with good detection performance by at least 4.8-5.2 percentage points. In particular, for the \"rumor\" and \"non-rumor\" categories, the accuracy, recall, and F1 values have been significantly improved, with each indicator exceeding 90%. However, it is important to acknowledge that this model still faces some limitations:  
1. Data dependency: Our methods are highly dependent on the quality and integrity of the collected data. When the data are sparse, noisy, or biased, the accuracy and generalization ability of the model may be affected to some extent. This limitation underscores the need for careful data preprocessing and validation. In future studies, we will further explore how to effectively process imperfect data to improve the robustness of the model, ensuring its reliability even in challenging data environments. 2. Computational complexity: For large datasets, our approach may require substantial computational resources and time costs. This can pose challenges in real-world applications, particularly those requiring real-time analysis. To address this limitation, we plan to focus on optimizing the algorithm design and improving computational efficiency in future work. By refining our models and leveraging advanced computational techniques, we aim to make our approach more scalable and suitable for large-scale data processing and real-time analysis scenarios. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Tag** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{3}{*}{DTRank} & F & \multirow{3}{*}{0.732} & 0.738 & 0.715 & 0.726 \\ \cline{2-5}  & T & & 0.726 & 0.749 & 0.737 \\ \hline \multirow{3}{*}{SVMRBF} & F & \multirow{3}{*}{0.818} & 0.822 & 0.812 & 0.817 \\ \cline{2-5}  & T & & 0.815 & 0.824 & 0.819 \\ \hline \multirow{3}{*}{DTC} & F & \multirow{3}{*}{0.831} & 0.847 & 0.815 & 0.831 \\ \cline{2-5}  & T & & 0.815 & 0.847 & 0.830 \\ \hline \multirow{3}{*}{BiGRU+} & F & \multirow{3}{*}{0.879} & 0.830 & 0.839 & 0.835 \\ \cline{2-5}  & T & & **0.907** & 0.902 & 0.905 \\ \hline \multirow{3}{*}{CNN+} & F & \multirow{3}{*}{**0.883**} & 0.873 & 0.795 & 0.832 \\ \cline{2-5}  & T & & 0.889 & **0.934** & **0.911** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results of different models on Weibo-16. In the next phase of our research, we will endeavor to extract emotional features from a broader range of sources, including images, text embedded in images, animations (GIFs), and videos. Furthermore, we will continue to enhance the classifier's ability to detect the latest data and real-time hot topics, thereby expanding the applicability and effectiveness of our rumor detection model. Conceptualization, W.J. and X.Z. ; methodology, W.J. ; software, F.Y. ; validation, W.J., F.Y. and K.R. ; formal analysis, B.W. ; investigation, W.J. ; resources, M.Z. ; data curation, X.Z. ; writing--original draft preparation, W.J. ; writing--review and editing, X.Z. ; visualization, W.J. and X.Z. ; supervision, M.Z. ; project administration, B.W. ; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.