The widespread proliferation of fake news on the Internet, especially in multi-modal formats, poses a substantial threat to society. Most deep learning-based approaches for fake news detection yield accurate predictions but lack explainability. Existing models focusing on explainability visualize key components from results or generate surface causes via Large Language Models. However, they can hardly provide the deep rationale behind the fabrication of fake news, which is indispensable for misinformation mitigation. Thus, we approach explainability from a different perspective, focusing on explaining how fake news is fabricated, which we term deceptive patterns, at its very source. First, four types of deceptive patterns are pre-established, namely Image Manipulation, Cross-modal Inconsistency, Image Repurposing and Others. Based on this, we propose GE-NSLM, a General Explainable Neuro-Symbolic Latent Model that integrates the power of Large Vision Language Models, which not only provides accurate judgments but also offers insights on deceptive patterns. Specifically, each deceptive pattern is represented as a binary learnable latent variable, obtained through amortized variational inference and weak supervision guided by logical rules. Experiments show GE-NSLM achieves competitive performance. More importantly, it provides interpretable insights into the underlying reasons why specific news items are fake.

In the current Internet landscape, the rampant spread of fake news, particularly in the form of multi-modal content, poses a great social threat. While automatic multi-modal fake news detection methods have shown promising results, the lack of explainability remains a significant challenge. Existing approaches provide superficial explainability by displaying learned important components or views from well-trained networks, but they often fail to uncover the implicit deceptive patterns that reveal how fake news is fabricated. To address this limitation, we begin by predefining three typical deceptive patterns, namely image manipulation, cross-modal inconsistency, and image repurposing, which shed light on the mechanisms underlying fake news fabrication. Then, we propose a novel Neuro-Symbolic Latent Model called NSLM, that not only derives accurate judgments on the veracity of news but also uncovers the implicit deceptive patterns as explanations. Specifically, the existence of each deceptive pattern is expressed as a two-valued learnable latent variable, which is acquired through amortized variational inference and weak supervision based on symbolic logic rules. Additionally, we devise pseudo-siamese networks to capture distinct deceptive patterns effectively. Experimental results on two real-world datasets demonstrate that our NSLM achieves the best performance in fake news detection while providing insightful explanations of deceptive patterns.

Recently, progress has been made towards improving automatic sarcasm detection in computer science. Among existing models, manually constructing static graphs for texts and then using graph neural networks (GNNs) is one of the most effective approaches for drawing long-range incongruity patterns. However, the manually constructed graph structure might be prone to errors (e.g., noisy or incomplete) and not optimal for the sarcasm detection task. Errors produced during the graph construction step cannot be remedied and may accrue to the following stages, resulting in poor performance. To surmount the above limitations, we explore a novel Iterative Augmenting Affective Graph and Dependency Graph (IAAD) framework to jointly and iteratively learn the incongruity graph structure. IAAD can alternatively update the incongruity graph structure and node representation until the learning graph structure is optimal for the metrics of sarcasm detection. More concretely, we begin with deriving an affective and a dependency graph for each instance, then an iterative incongruity graph learning module is employed to augment affective and dependency graphs for obtaining the optimal inconsistent semantic graph with the goal of optimizing the graph for the sarcasm detection task. Extensive experiments on three datasets demonstrate that the proposed model outperforms state-of-the-art baselines for sarcasm detection with significant margins.

Recently, the wanton dissemination of fake news on social media has adversely affected our lives, rendering automatic fake news detection a pressing issue. Current methods are often fully supervised and typically employ deep neural networks (DNN) to learn implicit relevance from labeled data, ignoring explicitly shared properties (e.g., inflammatory expressions) across fake news. To address this limitation, we propose a graph-theoretic framework, called Generalized Deep Markov Random Fields Framework (GDMRFF), that inherits the capability of deep learning while at the same time exploiting the correlations among the news articles (including labeled and unlabeled data). Specifically, we first leverage a DNN-based module to learn implicit relations, which we then reveal as the unary function of MRF. Pairwise functions with refining effects to encapsulate human insights are designed to capture the explicit association among all samples. Meanwhile, an event removal module is introduced to remove event impact on pairwise functions. Note that we train GDMRFF with the semi-supervised setting, which decreases the reliance on labeled data while maximizing the potential of unlabeled data. We further develop an Ambiguity Learning Guided MRF (ALGM) model as a concretization of GDMRFF. Experiments show that ALGM outperforms the compared methods significantly on two datasets, especially when labeled data is limited.