  Inflation is one of the most important macroeconomic indicators that have a
great impact on the population of any country and region. Inflation is
influenced by range of factors, one of which is inflation expectations. Many
central banks take this factor into consideration while implementing monetary
policy within the inflation targeting regime. Nowadays, a lot of people are
active users of the Internet, especially social networks. There is a hypothesis
that people search, read, and discuss mainly only those issues that are of
particular interest to them. It is logical to assume that the dynamics of
prices may also be in the focus of user discussions. So, such discussions could
be regarded as an alternative source of more rapid information about inflation
expectations. This study is based on unstructured data from Vkontakte social
network to analyze upward and downward inflationary trends (on the example of
the Omsk region). The sample of more than 8.5 million posts was collected
between January 2010 and May 2022. The authors used BERT neural networks to
solve the problem. These models demonstrated better results than the benchmarks
(e.g., logistic regression, decision tree classifier, etc.). It makes possible
to define pro-inflationary and disinflationary types of keywords in different
contexts and get their visualization with SHAP method. This analysis provides
additional operational information about inflationary processes at the regional
level The proposed approach can be scaled for other regions. At the same time
the limitation of the work is the time and power costs for the initial training
of similar models for all regions of Russia.
