A Joint Fake News Detection Model based on Multi-Features

Text analysis-based models have achieved outstanding results in fake news detection tasks in recent years, which is closely linked to the quantity and quality enhancement of feature information extracted from the text. Drawing upon the existing semantic detection frameworks, studies in this field concentrate on extracting various textual information through a solitary auxiliary feature, text stance feature or sentiment feature. However, it is challenging to depict the general attributes of the text using a single auxiliary feature, which frequently results in missing essential details and leaves problems with stance distortion and emotional resonance. To tackle the problem, this study proposes a joint model for identifying fake news, incorporating numerous textual characteristics. By extracting and blending various aspects of text features, i.e., semantic, stance and sentiment features, a more detailed and effective joint analysis of textual information is attained, resulting in improved performance in fake news detection. On the RumourEval-17 datasets, our model attains the Macro F1 Score of 0.891, surpassing current models for detecting rumors. Additionally, our model obtains a Macro F1 Score of 0.904 on the latest COVID-19 dataset, demonstrating strong competitiveness and promising prospects for fake news detection.