Regional Inflation Analysis Using Social Network Data

Abstract

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 a range of factors, including 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. It is hypothesised 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 users’ 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 used to analyse 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 visualisation 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.

Keywords

• inflation, regional inflation expectations, machine learning, BERT, social networks, monetary policy, neural network