# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 5 Experimental Results and Discussion
### Results
#### 5.2.2 Model Comparison  
Figures 4 and 5 display the accuracy scores of various machine learning models using different strategies on the PHEME and RUMDECT datasets, respectively. These diagrams demonstrate that most models reach a performance plateau with just 50-150 query instances, showcasing their ability to achieve commendable accuracy with a relatively small dataset size. Notably, most models exhibit consistent behavior and substantial improvements  
Figure 3: (**a**) On the PHEME dataset, the DW strategy achieved the best performance for the LDA model; however, its performance was the worst among all methods. As the query instances continue to increase, its performance approaches the baseline performance when the total number reaches 500. (**b**) The RFC model, which exhibited the best baseline performance on the RUMDECT dataset, demonstrates the ability to approach baseline performance with remarkable efficiency when utilizing the Unc(Ma) query strategy, converging in just 100 query instance. when provided with 50-150 well-selected data samples through active learning. Overall, the performance on the PHEME dataset lags behind that on the RUMDECT dataset, possibly due to the higher level of noise and class imbalance present in the PHEME dataset. Examining the results further, RFC yields the highest accuracy score in baseline and best convergence score in active learning among our models on both datasets. Although different query strategies may result in varying performance, they all demonstrate consistent convergence for RFC, which is true for models such as Ada, LR, GP, SVM(RBF) and SVM(Linear) as well. Several models, including LDA, MLP, QDA, and models trained by SGD. such as LR(SGD) and SVM(SGD), exhibited noticeable oscillations across various query strategies. In essence, these models experience fluctuations in their performance as the number of samples increases. Notably, the LR(SGD) and SVM(SGD) models display oscillations throughout the entire sampling process. This behavior may arise from a mismatch between the query strategies and the fundamental principle of random sampling behind SGD-based training. To mitigate this issue, it is possible to adopt query strategies that leverage gradient information, such as the Expected Gradient Length (EGL) strategy [45]. In the case of the LDA model applied to the PHEME dataset, the model's performance initially decreases during the early stages of training, then increases, and eventually converges. However, this phenomenon does not manifest on the RUMDECT dataset. We attribute this difference to the class imbalance in the PHEME dataset and the relatively small initial labeled dataset used for model training. To address this, we increased the number of initial datapoints in the PHEME dataset to 25, resulting in the disappearance of the fluctuations, as depicted in the Figure 6a. A similar trend was observed for the QDA models, which involve a greater number of model parameters. For both LDA and QDA models, active learning necessitates a larger labeled dataset to prevent fluctuations. In the case of the MLP model used with active learning, an interesting observation emerges as the query budget nears exhaustion, with a significant decrease in performance becoming evident. In situations where the sampling budget is increased to 500, as depicted in Figure 6b, two such performance dips are apparent. These dips do not disappear when changing the number of hidden units. We leave the investigation of the causes of this phenomenon to future work, along with the exploration of potential solutions. In the case of the Decision Tree Classifier (DTC) model, there is minimal variation between the Gini and Entropy criteria across all query strategies applied to the two datasets. However, it is noteworthy that an intriguing pattern emerges for the query strategies Unc(Lc), Unc(Ma), and Unc(En) as the number of samples increases, where the performance of the DTC model starts to decline.