STANCE CLASSIFICATION OF RUMOURS

Abstract

The accessibility and comfort of using social media have provided an optimal environment for people to expeditiously spread the information they have and sometimes without any knowledge of the authenticity of the information. Consequently, people inspect the stances reflected in the corresponding responses. To discover the certainty of rumour, stances are generally classified into 4 classes: support, deny, query and comment. The work presented brings forward a model for the Stance Classification of Rumours on a Twitter dataset which utilizes the newly introduced Capsule Network along with Multilayer Perceptron. The rule-based strategy is used to merge the output of both the networks in a way that utilizes the strength of the two networks. The performance of the proposed model is compared with the state-of-the-art Turing model and two baseline CNN models, one with parallel layer architecture and one without parallel layer architecture. The hybrid of Capsule Network and Muti-Layer Perceptron model surpasses the Turing model with regard to the macro average F1-score indicating better results across different sets of classes. Furthermore, the proposed capsule network model also outperforms both the CNN model in terms of both accuracy and macro average F1-score.