Multimodal fake news detection model using improved heuristic approach with optimal weighted integration and dilated adaptive deep learning

A lot of fake news is published with the help of television and social media. As a consequence, we are affected by the disinformation and misinformation spreading in our community. It is implemented to identify fake news creators to prevent spreading misinformation about the people. Thus, it is published like real news for the reputation and finances of an individual. It is challenging, because that is created by the integration of real and false details, and images are attached like originals to confuse the public. Few fake news detection tools are available to detect fake information. To solve this fake news spreading problem, an effectual multimodal fake news detection system is proposed based on the deep learning technique. In the beginning, the input image and text are gathered from benchmark data sources. Consequently, the deep features are extracted from raw images and text. The dilated Visual Geometry Group 16 (VGG16) is adopted to extract the image features and similarly, the text features are retrieved from the Dilated Text Convolutional Neural Network (DTCNN). After achieving two different features, it is upgraded into weighted fused features, in which the weight is tuned by an Adaptive Controlling Parameter-based Chameleon Swarm Algorithm (ACP-CSA). Finally, the fused features are fed as given to the Dilated Adaptive Deep Temporal Convolution Network with Bi-directional Long Short-Term Memory (DADTCN-Bi-LSTM) for predicting the fake news. The analysis is further performed by tuning the parameters in the model using developed ACP-CSA. The efficiency of the model is investigated and results are conducted. Thus, the analysis of the suggested system shows 95 regarding accuracy, sensitivity, and specificity. The analysis of the F1-score achieves the value of 90 in the developed model. Hence, the findings demonstrate that it achieves a better detection process to evade the existence of misinformation.