# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 5 Experimental Results and Discussion
### Results
#### 5.2.3 Analysis of Query Strategies  
In this subsection, we conduct a comprehensive comparative analysis of various active learning query strategies with the goal of gaining deeper insights into their respective advantages and disadvantages. This analysis aims to shed light on rumor detection for enhancing active learning query strategies in future research. On one hand, information-based query strategies have proven to be highly effective, especially for discriminative models characterized by linear classification boundaries. On the other hand, representative-based query strategies are better suited for models that rely on data diversity, such as DTC models. Interestingly, despite having the advantages of both characteristics, the combination of information-based and representative-based strategies does not yield superior performance. The uncertainty-based strategies perform well across most models on both datasets, and show no significant differences in performance. Based on the experimental results in Figures 4 and 5, these strategies are highly effective for discriminative models characterized by linear boundaries. However, they rely on estimating prediction uncertainties, and consequently cannot be applied to non-probabilistic models such as SVM(Linear) and SVM(SGD). Additionally, they tend to select non-representative instances in the feature space, such as outliers or noisy instances;as a result, their performance is lower on the noisy and imbalanced PHEME dataset compared to the RUMDECT dataset. Furthermore, because these strategies may exhibit a bias towards regions in the feature space where the model already has high confidence, they might inadvertently neglect crucial areas that require further exploration. As a result, a performance degradation phenomenon is observed for the DTC(Gini) and DTC(Entropy) models on both datasets, as well as for the KNN model on the PHEME dataset. Figures 4 and 5 illustrate that the QBC strategy consistently achieves the best performance across multiple models on the two datasets. Notably, even in challenging scenarios such as the DTC(Gini), DTC(Entropy), and KNN model on the PHEME dataset, the QBC strategy outperforms most other strategies. This outcome aligns with expectations, as QBC tends to select instances in which ensemble models exhibit the highest levels of disagreement, thereby promoting diversity in the selected instances. QBC is characterized by its stable performance and rapid convergence, making it adaptable to a wide range of machine learning models. In contrast, the ERR strategy is model-dependent and performs suboptimally when the model cannot provide reliable uncertainty estimates. Even in situations where uncertainty estimates are available, ERR tends to perform worse than QBC. This observation might be attributed to the fact that ERR is better suited for complex models dealing with high-dimensional data. The GD strategy, as a representative-based query strategy, does not consistently outperform the other strategies on either dataset. Its convergence is relatively slow, and its perfor  
Figure 6: (**a**) After increasing the initial amount of label data to 25, the LDA model does not have fluctuations. (**b**) After the MLP model increases the query budget to 500, two fluctuations appear. mance appears to depend on the dataset's characteristics. For instance, on the RUMDECT dataset, multiple models (including LR, GP, LDA, and MLP) using the GD strategy initially experienced a performance decrease, followed afterwards by an increase. However, this phenomenon did not manifest on the PHEME dataset. Regarding DW and QUIRE, which use a combination of the informative and representative strategies, our experiments indicate that they do not significantly outperform purely informative or representative approaches. In terms of running time, we can consider the example of LR and RFC running for ten rounds on two datasets. Table 6 provides a comparison of their running times.