In this paper we describe our participation to the SardiStance shared task held at EVALITA 2020. We developed a set of classifiers that combined text features, such as the best performing systems based on large pre-trained language models, together with user profile features, such as psychological traits and social media user interactions. The classification algorithms chosen for our models were various monolingual and multilingual Transformer models for text only classification, and XGBoost

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... the non-textual features. The combination of the textual and contextual models was performed by a weighted voting ensemble learning system. Our approach obtained the best score for Task B, on Contextual Stance Detection.