  Automation of social network data assessment is one of the classic challenges
of natural language processing. During the COVID-19 pandemic, mining people's
stances from public messages have become crucial regarding understanding
attitudes towards health orders. In this paper, the authors propose the
predictive model based on transformer architecture to classify the presence of
premise in Twitter texts. This work is completed as part of the Social Media
Mining for Health (SMM4H) Workshop 2022. We explored modern transformer-based
classifiers in order to construct the pipeline efficiently capturing tweets
semantics. Our experiments on a Twitter dataset showed that RoBERTa is superior
to the other transformer models in the case of the premise prediction task. The
model achieved competitive performance with respect to ROC AUC value 0.807, and
0.7648 for the F1 score.
