# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 6 Conclusions  
In this paper, we have conducted a comprehensive experimental comparative analysis assessing various rumor detection models in conjunction with diverse active learning query strategies. Our experimental results indicate that most supervised machine learning models can achieve model training with significantly fewer datapoints than the full training set, and that their performance can match or even surpass models trained on the complete dataset. Different supervised learning models exhibit varying performance under different active learning query strategies. This article presents experimental findings that identify the query strategies that enable different machine learning models to converge most rapidly. Our experimental results illustrate that RFC achieves the best performance on both datasets, while QBC emerges as the most suitable query strategy for most machine learning models in rumor detection. In addition to comparing model performance, this article discusses the runtimes of different strategies in order to further assess the pros and cons of different machine learning models when using various query strategies. This work can provide experimental guidance for the application of active learning in the field of rumor detection. As part of our future work, we plan to expand upon our current research by incorporating deep neural networks such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Graph Neural Networks (GNNs) for rumor detection. Additionally, we aim to explore the application of active learning in rumor detection within dynamic data streaming scenarios. Conceptualization, F.Y. and L.S. ; methodology, F.Y. and H.H. ; software, H.L. ; validation, F.Y., H.L. and H.H. ; formal analysis, F.Y. ; investigation, L.S. ; resources, H.L. ; data curation, F.Y. and H.L. ; writing--original draft preparation, F.Y. ; writing--review and editing, F.Y., L.S. and \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Dataset** & **Unc (Lc)** & **Unc (Ma)** & **Unc (En)** & **QBC** & **ERR** & **GD** & **QUIRE** & **DW** \\ \hline \multirow{2}{*}{LR} & PHEME & 360 & 214 & 352 & 600 & 125,347 & 358 & 547,705 & 484 \\  & RUMDECT & 381 & 239 & 370 & 417 & 176,304 & 380 & 625,944 & 845 \\ \hline \multirow{2}{*}{RFC} & PHEME & 783 & 847 & 813 & 3458 & 684,065 & 463 & 544,903 & 752 \\  & RUMDECT & 553 & 511 & 558 & 4042 & 465,379 & 523 & 763,072 & 987 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Running times (in seconds) of LR and RFC on the PHEME and RUMDECT datasets when employing different query strategies. H.H. ; visualization, H.L. ; supervision, F.Y. ; project administration, F.Y. ; funding acquisition, F.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by the 'Guangdong Province Overseas Renowned Teacher' project of department of science and technology of Guangdong province and the Zhongshan City Social Welfare and Basic Research Project \"Research on High-Precision Mark Point Localization Algorithm Based on Convolutional Neural Networks and Transfer Learning\" (No. 2022B2009). Not applicable. All data are contained within the article and the referenced papers. We express our gratitude to [PERSON] from Sun Yat-sen University for offering invaluable guidance and support throughout our thesis research. Additionally, we extend our appreciation to [PERSON] from Deakin University for providing extensive constructive feedback and valuable suggestions during the paper's writing process. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. The following abbreviations are used in this manuscript: