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Automation of social network data assessment is one of the classic challengesof natural language processing. During the COVID-19 pandemic, mining people'sstances from public messages have become crucial regarding understandingattitudes towards health orders. In this paper, the authors propose thepredictive model based on transformer architecture to classify the presence ofpremise in Twitter texts. This work is completed as part of the Social MediaMining for Health (SMM4H) Workshop 2022. We explored modern transformer-basedclassifiers in order to construct the pipeline efficiently capturing tweetssemantics. Our experiments on a Twitter dataset showed that RoBERTa is superiorto the other transformer models in the case of the premise prediction task. Themodel achieved competitive performance with respect to ROC AUC value 0.807, and0.7648 for the F1 score.

5q032e@SMM4H'22: Transformer-based classification of premise in tweets related to COVID-19