Stance Detection Based on User Connection

Abstract

The main purpose of stance detection is to mine users’ attitude towards topics or events.Different from other text classification tasks,the expression about stance is more obscure,and the attitude is more sensitive to users.The current stance detection methods mainly model the information of topic content itself,which ignores the user background information.However,the information of users and their preferences greatly affects the accurate mining of text information,which enables the potential information characteristics to be obtained through the associated user information.Therefore,this paper proposes a user connection-based stance detection model (USDM),which builds a user connection by constructing a graph of users,and mines similar text stance information under the same user from a global perspective by convolution operation.At the same time,attention mechanism is added to enhance user-aware text representation.The experimental results on the public dataset H&N14 show that the proposed model achieves better performance than other models.Meanwhile,ablation experiments show that user association information and attention mechanism play an important role in improving detection accuracy.

Keywords

• stance detection|graph convolutional neural network|attention mechanism