This work is aimed at creating a tool for filtering messages from Twitter users by the presence of mentions of coronavirus disease in them. For this purpose, a corpus of Russian-language tweets was created, which contains the part of 10 thousand tweets that are manually divided into several classes with different levels of confidence: potentially have covid, have covid now, other cases, and an unmarked part -- 2 million tweets on the topic of the pandemic. The paper presents the process of creating a corpus of tweets from the stage of data collection, their preliminary filtering and subsequent annotation according to the presence of disease description. Machine learning methods were compared according to classification task on tweets. It is shown that a model based on the XLM-RoBERTa topology with additional training on corpus of unmarked tweets gives the F1 score of 0.85 on binary classification task ("potentially have covid & have covid now" vs "other"). This is 12% higher relative to the simplest model using TF-IDF encoding and SVM classifier and 5% higher relative to the RuDR-BERT-based model.
The created toolkit will expand the feature space of models for predicting the spread of coronavirus infection and other pandemics by adding the dynamics of discussion on social networks, which characterizes people's attitudes towards it.