Multi-feature classification for fake news detection using multiscale and atrous convolution-based adaptive temporal convolution network

Abstract

In the exponential growth of social media platforms, Facebook, Twitter, YouTube and Instagram are the main sources for providing news and information about anything at anywhere. Sometimes, fake information is quickly spread by uploading from particular people affecting the media usage of people. In this research work, a novel deep learning-based framework is proposed to effectively detect fake news for enhancing the trust of social media users. At first, the required text data is gathered from the benchmark resources and given to the preprocessing stage. Then, the preprocessed data is fed into the feature extraction phase here, the Bidirectional Encoder Representations from Transformers (BERT), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN) mechanisms are utilized to effectively extract the meaningful information from the data and improve the accuracy. Also, it can generate three sets of BERT, temporal, and spatial features in the extraction phase and then given to the detection phase. Here, the Multiscale and Atrous Convolution-based Adaptive Temporal Convolution Network (MAC-ATCN) is used for ultimately identifying and categorizing the false information to ensure more reliable outcomes and decision-making processes. Additionally, the Modified Osprey Optimization Algorithm (MOOA) algorithm is employed to fine-tune the parameters to prevent overfitting issues when dealing with larger data. It helps to easily address the imbalanced dataset issues by varying the hyperparameters in the training process. Finally, the overall detection performance is validated with various performance measures and compared with existing works. Also, the developed method achieved better accuracy value for dataset 1 is 93.74 % and dataset 2 is 92.82%. By effectively identifying the fake news in social media can help users to make timely informed decisions. This helps to prevent the spread of misinformation and protects individuals from harmful consequences.