## 1 Introduction  
Currently, the global number of Internet users is 5.16 billion, with 4.76 billion social media users, constituting nearly 60% of the global population [1]. With the accelerated development of social networking platforms, such as Twitter and Facebook, the process of information sharing and dissemination has significantly accelerated [2]. While social media provides a platform for free and diverse forms of expression, it inadvertently facilitates the spread of misinformation, which often fails to reflect the true nature of events. For instance, the recently circulating claim that \"infection with Mycoplasma pneumonia will result in pneumonia and 'white lung'' not only misleads public perception and induces psychological anxiety but also triggers social panic, disrupts daily life, and severely undermines social stability. Therefore, to mitigate the detrimental effects of misleading content and to maintain social cohesion and stability, it is imperative to accurately identify false information. False information is typically defined as unsubstantiated content that lacks supporting evidence, authoritative sources, or official statements [3]. In recent years, scholars globally have demonstrated significant interest in the detection of false information [4]. Previous studies primarily employed manual feature extraction techniques to derive significant attributes from various aspects, including text content [5], user information [6], and communication characteristics [7], for false information detection. However, these methods depend on manual feature extraction, which necessitates extensive domain expertise and manual annotation efforts. The vast scale and complexity of social media datasets further present substantial technical challenges. With the progress of deep learning, scholars are adopting these models to autonomously identify effective characteristics for disinformation detection. [PERSON] et al. [8] were among the first to apply deep learning models to the field of false information detection. They input individual sentences into a recurrent neural network, using the hidden layer vectors to represent information features for false information detection. A key characteristic of false information is its rapid dissemination, often exhibiting a \"viral\" propagation pattern. Moreover, misinformation often spreads more extensively, quickly, and broadly than factual information, potentially causing significant confusion [3]. Consequently, researchers have sought to capture the temporal sequence characteristics of false information propagation by analyzing its propagation paths or networks on social media, thereby constructing time-series feature models [9; 10]. With the continued advancement of graph representation learning, false information detection utilizing graph-based approaches has become a prominent research focus. [PERSON] et al. [11] and [PERSON] et al. [12] constructed graph structures based on reply or retweet relationships, analyzing false information through edge-based feature aggregation. However, most deep learning-based rumor detection methods rely on reliable propagation structures (which may not always be available) and large-scale datasets to enable such analysis. These methods not only fail to account for the instability in misinformation propagation structures but also lead to substantial resource consumption during data preprocessing and model training. Our approach is based on the observation that the reinforcement of community structures plays a significant role in enhancing the efficacy of information dissemination, particularly in social networks, where rumor propagation structures demonstrate superior information representation capabilities [13]. However, existing deep learning-based rumor detection models often overlook the inherent uncertainty factors in real social networks when analyzing these propagation structures. Additionally, the effective utilization of original text features and their integration with other features remains an area requiring further exploration.