UstanceBR: a social media language resource for stance prediction

TL;DR

The corpus comprises 86.8 k labelled stances towards selected target topics, and extensive network information about the users who published these stances on social media, which is intended to provide initial baseline results for future studies in the field.

Abstract

This work introduces UstanceBR, a multimodal corpus in the Brazilian Portuguese Twitter domain for target-based stance prediction. The corpus comprises 86.8 k labelled stances towards selected target topics, and extensive network information about the users who published these stances on social media. In this article we describe the corpus multimodal data, and a number of usage examples in both in-domain and zero-shot stance prediction based on text- and network-related information, which are intended to provide initial baseline results for future studies in the field.