Open AccessInterpreting BERT-based stance classification: a case study about the Brazilian COVID vaccination
Author(s) -
Carlos Abel Córdova Sáenz,
Karin Becker
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbbd.2021.17867
Subject(s) - computer science , interpretation (philosophy) , word (group theory) , artificial intelligence , subject (documents) , mechanism (biology) , domain (mathematical analysis) , natural language processing , population , covid-19 , sentiment analysis , psychology , linguistics , sociology , mathematics , epistemology , world wide web , medicine , philosophy , mathematical analysis , demography , disease , pathology , infectious disease (medical specialty) , programming language
The actions to control the COVID-19 pandemics should be based on scientific facts. However, Brazil is facing a politically polarized scenario that has influenced the population’s behavior regarding social distance or vaccination issues. This paper addresses this subject by proposing a BERT-based stance classification model and an attention-based mechanism to identify the influential words for stance classification. The interpretation mechanism traces tokens’ attentions back to words, assigning word attention scores (absolute and relative). We use these metrics to assess if words with high attention weights correspond to domain intrinsic properties and contribute to the correct classification of stances. Our experiments revealed good results for stance classification (F1=0.752), and that 74% of the top-100 words with the highest absolute attention are representative of the arguments that support the investigated stances.