Class-weighted Dempster–Shafer in dual-level fusion for multimodal fake real estate listings detection

The CWDS-DLF was evaluated on the property listing website dataset and achieved an F1 score of 96% and an accuracy of 93%. A t-test confirms the significance of these improvements (<i>p</i> < 0.05), demonstrating the effectiveness of our method in detecting fake property listings. Compared to other models, including 2D-convolutional neural network (CNN), XGBoost, and various multimodal approaches, our model consistently outperforms in precision, recall, and F1-score. This underscores the potential of integrating multimodal analysis with sophisticated fusion techniques to enhance the detection of fake property listings, ultimately improving consumer protection and operational efficiency in online real estate platforms.