# UstanceBR: a social media language resource for stance prediction

arXiv: https://arxiv.org/abs/2312.06374 · HTML: https://mlsanity.com/paper/2312.06374/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter](https://mlsanity.com/paper/2005.00388/) (2020)
- [Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection](https://mlsanity.com/paper/2103.01664/) (2021)
- [X-Stance: A Multilingual Multi-Target Dataset for Stance Detection](https://mlsanity.com/paper/2003.08385/) (2020)
- [A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19](https://mlsanity.com/paper/2009.01188/) (2020)
- [AraStance: A Multi-Country and Multi-Domain Dataset of Arabic Stance Detection for Fact Checking](https://mlsanity.com/paper/2104.13559/) (2021)
- [Stance Prediction for Contemporary Issues: Data and Experiments](https://mlsanity.com/paper/2006.00052/) (2020)
- [#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection](https://mlsanity.com/paper/2007.14936/) (2020)
- [ExaASC: A General Target-Based Stance Detection Corpus in Arabic Language](https://mlsanity.com/paper/2204.13979/) (2022)
- [A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter](https://mlsanity.com/paper/2206.14619/) (2022)

## Abstract (arXiv)

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.