Assessment of bidirectional transformer encoder model and attention based bidirectional LSTM language models for fake news detection

Fake news arouses to be untrue with the point of deceiving it openly which is now viewed as the greatest threat to society by cultivating the political division and doubts in government. Since this kind of news is disseminated in sheer volume through social media, driving the improvement of strategies for the recognizable proof of false news is necessary. Therefore, this study focuses on text analytics to derive the hidden properties of stylistic content to detect fake and real news. An erudite literature study of fake news detection diverted towards issues such as attention, context, and parallelization. In this same direction, the assessment evaluates the sequential memory-based deep learning model in comparison to the parallel memory-based deep learning model. For sequential, Long Short-Term Memory (LSTM), Bi-Directional LSTM, and Attention-based Bi-directional LSTM are taken into consideration. Besides, for parallel, the transformer-based BERT model is examined. To identify the efficacy of applied approaches, four datasets are taken from diverse domains such as political news, entertainment news, satire news, conspiracy news, and global pandemic news. The experimental analysis of real-world information demonstrates that the pre-trained transformer encoder-based BERT model outperforms with a quite significant margin of improvement. Also, as inspected Attention-based Bi-directional approach provides state-of-the-art results with good training accuracy.