# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
###### Abstract
These methods encompass a range of approaches, including traditional machine learning models [3; 9] and neural network-based approaches [10; 11; 12], which all follow a supervised learning paradigm. In this paradigm, posts are first transformed into representations, which are then fed into a supervised learning model guided by ground-truth labels. Traditional machine learning-based approaches often rely on hand-crafted features, while neural network-based models automatically learn latent deep feature representations of rumors. However, both approaches require a sizable annotated dataset, such as RUMDECT [10] or PHEME [13], for training reliable classifiers. While the aforementioned methods have demonstrated promising results, they face several significant challenges, as highlighted by previous research in the field of automatic rumor detection. One of the most critical challenge pertains to the labor-intensive and costly nature of constructing rumor datasets [14]. Labeling rumors within the ever-flowing stream of social media is a resource-intensive task associated with substantial costs. To illustrate this, consider the Sina Community Management Center's rumor reporting process ([[https://service.account.weibo.com/](https://service.account.weibo.com/)]([https://service.account.weibo.com/](https://service.account.weibo.com/)), accessed on 3 November 2023) depicted in Figure 1.  
A social media user must navigate through three stages for rumor reporting: the reporting stage, the evidence stage, and the results announcement stage. The evidence stage demands that the reporting user provide proof that the post in question is indeed a rumor. Subsequently, this evidence is scrutinized by experts from the Sina platform. This process is both time-consuming and financially burdensome. Moreover, the rapid advancement of artificial intelligence (AI), particularly the emergence of generative models such as Generative Adversarial Networks (GANs) and diffusion models, has led to an increase in manipulated multimodal rumors. These rumors may incorporate image, audio, and video data, rendering them increasingly challenging for ordinary social media users to differentiate from genuine content. A notable example is the use of DeepFakes, which leverage deep learning models to fabricate audio and video clips of real individuals uttering or performing actions that never actually occurred. This makes rumors appear both more realistic and harder to discern [15; 16]. Furthermore, certain rumors may contain domain-specific knowledge and can only be debunked by experts in the respective field. Annotation of previously unseen rumors often requires in-depth domain knowledge. A notable example occurred during the COVID-19 pandemic, when rumors such as \"5G caused the virus\" or \"facemasks do not work\" had to be confirmed as false by professional or authoritative medical experts rather than ordinary social media users. In more challenging scenarios, slight modifications to aspects of a non-rumor can lead to the creation of new and more convincing rumors. For instance, altering details such as the timing, location, or individuals associated with a non-rumor event can result in the fabrication of a convincing rumor. In such situations, it becomes significantly more arduous for experts to distinguish rumors from normal posts, making it a time-consuming and domain knowledge-intensive task. Despite the growing volume of posts on social media platforms, including rumors, obtaining high-quality, large-scale, and authoritative benchmark datasets remains a daunting task. In comparison to benchmarks such as ImageNet [17], which contains 14,197,122 images and serves as a standard in visual object recognition, Table 1 shows that datasets used in recent research on rumor detection are relatively small in scale or confined to specific rumor categories. This discrepancy underscores the need for developing comprehensive benchmark datasets, particularly in the current revolutionary era in deep learning.