  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.
