BerConvoNet : a deep learning framework for fake news classification

Abstract

Fake news has become a major concern over the Internet. It influences people directly and should be identified. In the recent years, various Machine Learning (ML) and Deep Learning (DL) based data-driven approaches have been suggested for fake news classification. Most of the ML based approaches use hand-crafted features extracted from input textual content. Moreover, in DL based approaches, an efficient word embedding representation of input data is also a major concern. This paper presents a deep learning framework, BerConvoNet, to classify the given news text into fake or real with minimal error. The presented framework has two main building blocks: a news embedding block (NEB) and a multi-scale feature block (MSFB). NEB uses Bidirectional Encoder Representations from Transformers (BERT) for extracting word embeddings from a news article. Next, these embeddings are fed as an input to MSFB. The MSFB consists of multiple kernels (filters) of varying sizes. It extracts various features from news word embedding. The output of MSFB is fed as an input to a fully connected layer for classification. To validate the performance of BerConvoNet, several experiments have been performed on four benchmark datasets and various performance measures are used to evaluate the results. Furthermore, the ablative experiments with respect to news article embedding, kernel size, and batch size have been carried out to ensure the quality of prediction. Comparative analysis of the presented model is done with other state of the art models. It shows that BerConvoNet outplays other models on various performance metrics.