RumEval2020-An Effective Approach for Rumour Detection with a Deep Hybrid C-LSTM Model

Abstract

For the online and offline world, the widespread rumours on social media have created a tremendous effect on society. In this paper, our primary focus to develop a useful deep learning model for the classification multi-class and real-world rumour dataset. In existing investigations, RumourEval17 have released by the research community, which mainly interest in automated validation of fake content has escalated. After some time, as the insecurity imposed by “fake news” has become a mainstream concern. However, automatic support for rumour verification system remains in its preliminary stage. Subsequently, the main aim of introducing RumourEval-2019 (SemEval 2019) was to determine the veracity of rumours. In this paper, we have designed our proposed deep learning model for classification of rumours using real-world multi-class rumours dataset: Twitter and Reddit. Classification results demonstrate that our proposed model provides state-of-the-art results as compared to existing benchmarks. We have achieved an accuracy of 82.40% for subtask A and 81.04% for subtask B. Our classification results are better as compared to previous RumourEval studies using twitter & Reddit dataset. Classification results motivate the researchers to use our proposed model for future research in the filed of rumour detection.