UstanceBR: a social media language resource for stance prediction
This work provides a new language resource for stance prediction in social media, but it is incremental as it focuses on a specific domain and dataset.
The authors introduced UstanceBR, a multimodal corpus of 86.8k labeled stances in Brazilian Portuguese from Twitter, and provided baseline results for stance prediction tasks.
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.