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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 ofhuman 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, usingsymbols such as hashtags to represent their attitudes. However, many userschoose not to attach hashtags to their messages, use a different language, orshow their position only indirectly. Thus, automatically identifying theiropinions becomes a more challenging task. To uncover these implicitperspectives, we propose a collaborative filtering model based on GraphConvolutional Networks that exploits the textual content in messages and therich connections between users and topics. Moreover, our approach only requiresa 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 inChile in 2020 and 2022. Using two large Twitter datasets, our model achievesimprovements of 3.4% in recall and 3.6% in accuracy over the baselines.

Stance Inference in Twitter through Graph Convolutional Collaborative Filtering Networks with Minimal Supervision