Deciphering Rumors: A Multi-Task Learning Approach with Intent-aware Hierarchical Contrastive Learning (2024)

Abstract: Social networks are rife with noise and misleading information, presenting multifaceted challenges for rumor detection. In this paper, from the perspective of human cognitive subjectivity, we introduce the mining of individual latent intentions and propose a novel multi-task learning framework, the Intent-Aware Rumor Detection Network (IRDNet). IRDNet is designed to discern multi-level rumor semantic features and latent user intentions, addressing the challenges of robustness and key feature mining and alignment that plague existing models. In IRDNet, the multi-level semantic extraction module captures sequential and hierarchical features to generate robust semantic representations. The hierarchical contrastive learning module incorporates two complementary strategies, event-level and intent-level, to establish cognitive anchors that uncover the latent intentions of information disseminators. Event-level contrastive learning employs high-quality data augmentation and adversarial perturbations to enhance model robustness. Intent-level contrastive learning leverages the intent encoder to capture latent intent features and optimize consistency within the same intent while ensuring heterogeneity between different intents to clearly distinguish key features from irrelevant elements. Experimental results demonstrate that IRDNet significantly improves the effectiveness of rumor detection and effectively addresses the challenges present in the field of rumor detection.

## introduction the contemporary era of social networks witnesses unprecedented information dissemination, which concurrently leads to an accelerated spread of rumors. the complexity of this social phenomenon presents significant challenges in rumor detection @xcite . fundamentally, the belief in rumors is contingent upon two criteria: "able to believe" and "willing to believe". the former involves the diversity of information in social net-* * corresponding author figure 1 : the results of traditional data augmentation methods are similar in semantic structures but inconsistent with the intentions of the information disseminator, while data augmentation based on large language models has been proven to be effective. inspired by this, we propose the intent-aware rumor detection network (irdnet), which designs a new contrastive learning method targeting human subjectivity, model robustness, and consistency of key features, and constructs cognitive anchors to mine the potential intentions of information disseminators. it includes the following key components: in semantic feature extraction module, we design a semantic extractor that employs pre-trained models to enhance the model's semantic and contextual relevance handling. additionally, it combines bilstm and capsule networks to achieve sequential and hierarchical feature learning, strengthening the model's ability to capture deep semantic features. in intent-aware hierarchical contrastive learning module, cognitive anchors are mainly implemented through two levels of contrastive learning: event-level contrastive learning and intent-level contrastive learning. in event-level contrastive learning, we combine adversarial training techniques to build high-quality data augmented representations to enhance the robustness of the model. in intent-level contrastive learning, by building intent-aware pairs and leveraging intent-level contrastive learning, it can separate individual intents and capture the main intent in a fine-grained manner while ensuring intent consistency. in general, the main contributions of this paper are as follows: ## related work previous rumor detection methods primarily relied on feature engineering techniques to extract features from existing rumor instances @xcite @xcite @xcite . these studies focused on utilizing features such as post content, user profiles, and propagation patterns to train supervised classifiers. however, the effectiveness of this approach heavily depended on the quality of feature engineering, leading to potential issues with model generalization across diverse datasets. deep learning focuses on extracting deep key features, such as using recurrent neural networks @xcite and convolutional neural networks @xcite @xcite to extract important information from text content. in addition, capsule networks have been used to address the limitations of cnn @xcite @xcite . capsule networks use a dynamic routing mechanism to replace the maximum pooling operation of cnn to retain spatial information and capture key patterns in text. @xcite . in recent advancements, graph neural network models have been employed to exploit valuable features from content semantics and propagation structures. these models encode conversation threads by modeling propagation trees @xcite @xcite and propagation graphs @xcite @xcite , resulting in higher-level representations. nevertheless, when data is limited in the early stages of rumor propagation, the above methods may not fully exploit their advantages @xcite . contrastive learning (cl) can alleviate the problem of data scarcity, especially by improving data quality and utilizing limited labeled data, and has gradually been applied to rumor detection @xcite @xcite . in addition, multi-task learning (mtl) is a strategy that improves the generalization ability of the model by leveraging the shared knowledge between multiple interrelated tasks @xcite , which has also been widely explored and used in the field of rumor detection @xcite . early studies @xcite ) mainly emphasize shared features and improve the effectiveness of rumor detection by promoting feature interactions between different tasks. recent studies @xcite @xcite simultaneously consider related tasks, such as rumor detection and stance detection, to understand the text more comprehensively. some studies @xcite @xcite also attempt to apply mtl in multimodal environments, fusing multiple information sources such as text, images, or audio, aiming to more effectively capture the multidimensional properties of rumor propagation, thereby improving the overall performance of rumor detection. ## problem statement in the task of rumor detection, we define a series of events in the datasets, each event e i is linked to a source tweet s i and all associated comment contents, denoted as e @xmath0, x 1 , x 2 , ..., x n }, where s i can also be represented as x 0 , and n represents the number of relevant comments in the discussion thread. the task goal is to build a robust rumor detection classifier by fully mining and learning the critical feature representations of rumor-related events on social networks, which can be expressed as f : e @xmath1, where y i belongs to the categories of non-rumor, false rumor, true rumor, and unverified. ## methodology as shown in figure 2 , we propose an intent-aware rumor detection network (irdnet) using a multitask training framework, including supervised semantic feature extraction tasks and self-supervised intent-aware hierarchical contrastive learning tasks and jointly optimize them by fusing supervised and self-supervised loss functions. each module's principles and specific details will be introduced below. ## conclusion and future work this paper proposes an intent-aware rumor detection network (irdnet) that combines supervised semantic feature extraction with self-supervised intent-aware hierarchical contrastive learning in a multi-task framework to address the challenges of subjectivity, robustness, and consistency in the current field. it contains two modules: the semantic feature extraction module captures multi-level semantic information and enhances the model's ability to understand context. the intent-aware hierarchical contrastive learning module introduces two strategies: event-level contrastive learning enhances robustness through data augmentation and adversarial training, and intent-level contrastive learning captures subjective intent features by designing an intent encoder, ensuring intent consistency and improving feature discrimination while maintaining robustness. experimental results show that irdnet performs better noise resistance and critical feature capture than baseline methods in comparative experiments and early rumor detec-tion. future research will focus on the following two aspects. first, the proposed contrastive learning approach will be extended to a broader range of classification tasks to verify its generality and effectiveness in diverse tasks, expanding its applicability. second, efforts will focus on addressing the current limitations of large language models (llms) in rumor detection. specifically, we will work towards enhancing the robustness and detection accuracy of llms in dynamic misinformation environments, while also ensuring higher reliability and consistency in rumor detection tasks.