In the field of fake news detection, deep learning techniques have emerged as superior performers in recent years. Nevertheless, the majority of these studies primarily concentrate on either unimodal feature-based methodologies or image-text multimodal fusion techniques, with a minimal focus on the fusion of unstructured text features and structured tabular features. In this study, we present SGAMF, a Sparse Gated Attention-based Multimodal Fusion strategy, designed to amalgamate text features and auxiliary features for the purpose of fake news identification. Compared with traditional multimodal fusion methods, SGAMF can effectively balance accuracy and inference time while selecting the most important features. A novel sparse-gated-attention mechanism has been proposed which instigates a shift in text representation conditioned on auxiliary features, thereby selectively filtering out non-essential features. We have further put forward an enhanced ALBERT for the encoding of text features, capable of balancing efficiency and accuracy. To corroborate our methodology, we have developed a multimodal COVID-19 fake news detection dataset. Comprehensive experimental outcomes on this dataset substantiate that our proposed SGAMF delivers competitive performance in comparison to the existing state-of-the-art techniques in terms of accuracy and $F_{1}$ score.