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This work introduces UstanceBR, a multimodal corpus in the BrazilianPortuguese Twitter domain for target-based stance prediction. The corpuscomprises 86.8 k labelled stances towards selected target topics, and extensivenetwork information about the users who published these stances on socialmedia. In this article we describe the corpus multimodal data, and a number ofusage examples in both in-domain and zero-shot stance prediction based on text-and network-related information, which are intended to provide initial baselineresults for future studies in the field.

UstanceBR: a social media language resource for stance prediction