On April 30th, 2023, the 11th International Workshop on Natural Language Processing for Social Media (SocialNLP) took place in Texas, USA, in conjunction with the ACM Web Conference 2023. Zhiwei Zhou and Erick Elejalde from the L3S Research Center participated with the paper “Stance Inference in Twitter through Graph Convolutional Collaborative Filtering Networks with Minimal Supervision”.
Stance Inference in Twitter through Graph Convolutional Collaborative Filtering Networks with Minimal Supervision
Abstract: Social Media (SM) has become a stage for people to share thoughts, emotions, opinions, and almost every other aspect of their daily lives. This abundance of human interaction makes SM particularly attractive for social sensing. Especially during polarizing events such as political elections or referendums, users post information and encourage others to support their side, using symbols such as hashtags to represent their attitudes. However, many users choose not to attach hashtags to their messages, use a different language, or show their position only indirectly. Thus, automatically identifying their opinions becomes a more challenging task. To uncover these implicit perspectives, we propose a collaborative filtering model based on Graph Convolutional Networks that exploits the textual content in messages and the rich connections between users and topics. Moreover, our approach only requires a small annotation effort compared to state-of-the-art solutions. Nevertheless, the proposed model achieves competitive performance in predicting individuals’ stances. We analyze users’ attitudes ahead of two constitutional referendums in Chile in 2020 and 2022. Using two large Twitter datasets, our model achieves improvements of 3.4% in recall and 3.6% in accuracy over the baselines.