DEAR : Disentangled Event-Agnostic Representation Learning for Early Fake News Detection (2025)

Abstract: Detecting fake news early is challenging due to the absence of labeled articles for emerging events in training data. To address this, we propose a Disentangled Event-Agnostic Representation (DEAR) learning approach. Our method begins with a BERT-based adaptive multi-grained semantic encoder that captures hierarchical and comprehensive textual representations of the input news content. To effectively separate latent authenticity-related and event-specific knowledge within the news content, we employ a disentanglement architecture. To further enhance the decoupling effect, we introduce a cross-perturbation mechanism that perturbs authenticity-related representation with the event-specific one, and vice versa, deriving a robust and discerning authenticity-related signal. Additionally, we implement a refinement learning scheme to minimize potential interactions between two decoupled representations, ensuring that the authenticity signal remains strong and unaffected by event-specific details. Experimental results demonstrate that our approach effectively mitigates the impact of event-specific influence, outperforming state-of-the-art methods. In particular, it achieves a 6.0% improvement in accuracy on the PHEME dataset over MDDA, a similar approach that decouples latent content and style knowledge, in scenarios involving articles from unseen events different from the topics of the training set.

## introduction social media platforms provide a convenient way for the public to create, distribute, and absorb a wide range of information. however, the authenticity of information shared on these platforms has become a growing concern. when users cannot * corresponding author. advances in natural language processing (nlp) have facilitated the development of fake news detection methods (as described in section 4.1), which have shown significant performance in controlled scenarios, as illustrated in figure 1 (b). however, on social media, sudden explosive events often lead to a flood of related news. in the early stage of news dissemination, resources related to newly emerging events are usually limited, and authenticity annotations for those news articles are often unavailable. this makes it challenging to quickly train new fake news detection models. meanwhile, detection models trained on existing annotated data typically perform poorly on emerging ''unseen'' events during inference, as shown in figure 1(c ). therefore, it highlights the challenge of quickly detecting and blocking fake news articles about these newly emerging events before they spread widely. recent methods of fake news detection address this challenge by treating distinct events as various domains and leveraging domain adaptation techniques (as described in section 4.2). these methods aim to bridge the semantic gaps between events by enhancing their correlations. however, they require a portion of data from the target domain during training, making them inapplicable for the early fake news detection scenario with ''unseen'' events. moreover, since newly emerging events on social media appear frequently, it is impractical to adapt the model each time a new event occurs. therefore, a model is needed to identify fake news immediately from most newly emerging ''unseen'' events. based on this analysis, we assume that a news article contains two key latent attributes: authenticity-related knowledge, which determines figure 1 : illustration of different fake news detection scenarios: (a) the training set, composed of news articles from two existing events. (b) this represents a type of testing set including articles related to the same events as the training set, referred to as ''seen'' events. in contrast, (c) illustrates another type of testing set containing articles about new events not present in training, showcasing a detection scenario for ''unseen @xcite if it is fake or real, and event-specific knowledge, which varies significantly among different events. we explore the performance of fake news detection for newly ''unseen'' events by utilizing the latent authenticity-related knowledge and mitigating the impact of the event-specific knowledge. building on these insights from our exploration, we propose a disentangled event-agnostic representation (dear) learning approach to address the challenges of early fake news detection for newly ''unseen'' events (explained in section 2). it begins with an adaptive multigrained semantic encoder for contextual representation extraction. next, a disentanglement architecture is utilized to decouple the latent authenticity-related and event-specific representations of news content. to enhance the disentanglement effect, we introduce a cross-perturbation mechanism that perturbs the generated authenticityrelated representation with the event-specific one, and vice versa. this directs the discriminators to focus on each corresponding knowledge, thereby improving their robustness. finally, a refinement learning scheme is employed to further reduce potential interactions between the two decoupled representations, ensuring the authenticity-related signal remains strong and unaffected by event-specific details. we demonstrate the effectiveness of our approach through comprehensive experiments conducted on datasets, showcasing its superiority over established methods (outlined in section 3). ## conclusion in this paper, we introduce dear, an early fake news detection approach that leverages a disentanglement architecture to separate authenticity-related and event-specific knowledge. our approach employs interactive crossperturbation and refinement learning techniques to enhance the disentanglement effect, minimizing interactions between the decoupled representations. an adaptive multi-grained semantic encoder, based on bert, generates hierarchical and fine-grained textual representations. experimental results across multiple datasets demonstrate the effectiveness of dear in mitigating event-specific knowledge for fake news detection, outperforming state-of-the-art methods. as future work, we plan to extend the proposed disentangled methodology to address multi-modal fake news detection, exploring the possibility of mitigating event-specific knowledge in the multi-modality context.