Rumour Stance Classification using A Hybrid of Capsule Network and Multi-Layer Perceptron

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Akshi Kumar, et. al.

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. This paper 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 surpassing the state-of-the-art with regard to the macro average F1-score indicating better results across different sets of classes.

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