Pandemic rumor identification on social networking sites: A case study of COVID-19

Digital Rumors, because of the ease and innovations in social networking technologies, has become an important issue. These rumors become a critical issue in a disaster, epidemic, or pandemic. Considering classification power of conventional and deep learning techniques, we propose a hybrid learning technique that identifies rumors effectively. For this, TF-IDF description has been used to build a stack of multiple conventional learning techniques; logistic regression, Naïve Bayes, and random forest. Whereas, word-embedding features have been used for purpose of deep learning; LSTM and LSTM-RNN. The combination of LSTM and RNN makes this study unique in the field of rumor detection. With LSTM and RNN gated architectures, huge series rumor tweets may be efficiently managed. To aggregate the decisions, the labels of deep learning and the stack of conventional learning have been combined using majority voting based ensemble classification. To evaluate the performance of the proposed technique, we used publically available standard COVID-19 RUMOR dataset. The proposed technique obtains 99.02% accuracy, which shows its effectiveness. The dataset utilized and the ensemble model created for rumor identification distinguish our work from existing methods.