Deceptive News Content Detection using a Hybrid Transformer-based and Deep Learning Model with Explainability

The growth of social media platforms has facilitated knowledge dissemination. The ability of misinformation to affect elections, public opinion, and instigate instability makes it a dangerous threat to civilization that spreads rapidly. For an informed and reliable information ecology to survive, the ability to identify deceptive information in an extensive variety of languages is essential. The Transformer based pretrained language models (TB-PLMs) like Distilled Bidirectional Encoder Representations from Transformers (DistilBERT), ALite BERT (ALBERT) and Robustly Optimized BERT Pretraining Approach (RoBERTa) versions of the BERT model with a deep neural network structures such as Bi-directional Gated Recurrent Unit (BiGRU) and Convolution Neural Network (CNN) is used for the identification of deceptive news in English. The dataset utilized for the challenge consists of a combination of LIAR, and Fake/Real news dataset, resulting in a Combined Corpus (CC) dataset about politics. TB-PLMs are optimized to understand the semantic linkages and contextual information found in the dataset. BiGRU and CNN layers are used to capture the dependencies between neighboring characters in the text. The experimental findings show that the RoBERTa+BiGRU model performs better in comparison with all the other models in identifying English deceptive news with an accuracy of 99.04%. The results obtained reflect that RoBERTa+BiGRU has rise of 0.06% in accuracy from the base RoBERTa model. Also, the results of proposed work on DistilBERT+BiGRU and RoBERTa+BiGRU model performs well based on features (class 0 and class 1) while utilizing Local Interpretable Model-agnostic Explanations (LIME) implementation to clarify the target labels which can facilitate valid data extraction and processing to successfully counteract deceptive information.