When Tweets Get Viral – A Deep Learning Approach for Stance Analysis of Covid-19 Vaccines Tweets by Brazilian Political Elites

por Lorena G. Barberia, Pedro Henrique S. Schmalz, Norton T. Roman


Social media platforms are crucial for understanding public opinion about policy issues. In this regard, detecting stance in Twitter posts is a vital tool. In this study, we built a corpus of tweets from 2020 and 2021, annotated with stance towards COVID-19 vaccines and vaccination, and test BERTimbau as a way to automatically detect stance in such tweets. Our model reached 86% accuracy in 2020, 77% in 2021, and 79% in the combined 2020/2021 set. Our results also highlight the time-dependent nature of data distribution and, as a consequence, stance classification. Therefore, this research also contributes to the field by shedding some light on the existing methodological challenges in analyzing complex public policy debates over time.