Abstract: The ubiquity of the contemporary language understanding tasks gives relevance
to the development of generalized, yet highly efficient models that utilize all
knowledge, provided by the data source. In this work, we present SocialBERT -
the first model that uses knowledge about the author's position in the network
during text analysis. We investigate possible models for learning social
network information and successfully inject it into the baseline BERT model.
The evaluation shows that embedding this information maintains a good
generalization, with an increase in the quality of the probabilistic model for
the given author up to 7.5%. The proposed model has been trained on the
majority of groups for the chosen social network, and still able to work with
previously unknown groups. The obtained model, as well as the code of our
experiments, is available for download and use in applied tasks.