# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
###### Abstract
This epoch is frequently characterized by the phrase \"Data is the new oil\" [24], signifying the pivotal role of data in driving advancements across various tasks and applications through data-driven learning approaches. These approaches place heightened demands on \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Data Source** & **Rumor** & **Non-Rumor** \\ \hline KWON [18] & Twitter & 47 & 55 \\ PHEME [19] & Twitter & 1972 & 3830 \\ Medieval [20] & Twitter & 9000 & 6000 \\ Twitter15 [21; 22] & Twitter & 1116 & 374 \\ Twitter16 [10; 22] & Twitter & 618 & 205 \\ MULTII [23] & Sina Weibo & 4749 & 4779 \\ RUMDECT [10] & Twitter & 498 & 494 \\ Sina Weibo & 2313 & 2351 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Common datasets use by state-of-the-art rumor detection research. Figure 1: The workflow of rumor reporting on the Weibo platform, showing the three stages by which a Sina user can confirm a rumor. The green English corresponds to the translation of the Chinese text just above. both the quality and quantity of data. It is crucial to recognize that the size and quality of datasets wield a profound influence on the performance and scalability of state-of-the-art (SOTA) rumor detection models [25]. In addition to the aforementioned challenges around labeling rumors and the limited scale of datasets, the performance of learned models may deteriorate due to conceptual drift. This phenomenon occurs when the distribution of features related to rumors undergoes changes over time. Typically, mitigating conceptual drift requires the continuous annotation of new datapoints and model updates. Unfortunately, this practice can be both costly and impractical. In summary, the field of automatic rumor detection faces a significant challenge in large-scale data annotation. To address the challenges associated with rumor detection, an intuitive idea is to selectively label valuable data instead of annotating the entire dataset for training rumor detection models. Active Learning (AL) has emerged as a promising solution to overcome the key challenges outlined earlier. As a subfield of machine learning, active learning aims to create efficient training datasets by iteratively enhancing model performance through strategic sample selection. The goal is to achieve or even surpass the expected model performance with as few labeled samples as possible [26]. Active learning recognizes that not all samples in a dataset are equally crucial for training a machine learning model. Therefore, it intelligently selects a subset of the dataset for labeling by an oracle, such as a human annotator, to optimize model performance. This approach mitigates the labeling bottleneck and minimizes the costs associated with acquiring labeled data. Consequently, active learning is well-suited for rumor detection scenarios, in which a surplus of unlabeled data is available from real social media streams while labeled data remain a costly resource. Despite the existence of comparative studies across various tasks and domains, active learning has not been extensively explored in the context of rumor detection. In this work, we present a comparative analysis of active learning techniques for rumor detection on social media platforms, aiming to answer the following key questions:  
1. Can active learning effectively reduce labeling costs in the context of rumor detection while maintaining high performance? 2. Which active learning query strategies are most suitable for specific rumor detection methods? This research seeks to shed light on the potential of active learning in improving rumor detection while addressing the practical challenges associated with labeling large datasets. Hence, we evaluate the feasibility of utilizing active learning for rumor detection on social media platforms. To assess the effectiveness of active learning, we conduct a comparative analysis of multiple supervised machine learning methods.