A Hybrid Model for Effective Fake News Detection with a Novel COVID-19 Dataset

Kaliyar, R. K.; Goswami, A.; Narang, P.

Due to the increasing number of users in social media, news articles can be quickly published or share among users without knowing its credibility and authenticity. Fast spreading of fake news articles using different social media platforms can create inestimable harm to society. These actions could seriously jeopardize the reliability of news media platforms. So it is imperative to prevent such fraudulent activities to foster the credibility of such social media platforms. An efficient automated tool is a primary necessity to detect such misleading articles. Considering the issues mentioned earlier, in this paper, we propose a hybrid model using multiple branches of the convolutional neural network (CNN) with Long Short Term Memory (LSTM) layers with different kernel sizes and filters. To make our model deep, which consists of three dense layers to extract more powerful features automatically. In this research, we have created a dataset (FN-COV) collecting 69976 fake and real news articles during the pandemic of COVID-19 with tags like social-distancing, covid19, and quarantine. We have validated the performance of our proposed model with one more real-time fake news dataset: PHEME. The capability of combined kernels and layers of our C-LSTM network is lucrative towards both the datasets. With our proposed model, we achieved an accuracy of 91.88% with PHEME, which is higher as compared to existing models and 98.62% with FN-COV dataset.