Regional inflation analysis using social network data

ArXiv ID: 2403.00774 “View on arXiv”

Authors: Unknown

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 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.

Keywords: Inflation Expectations, Natural Language Processing (NLP), BERT, Social Media Analysis, SHAP, Macro / Fixed Income

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper primarily applies established NLP techniques (BERT, SHAP) rather than developing new mathematical theory, but it demonstrates strong empirical rigor with a large-scale dataset (8.5M posts), robust methodology, and clear operational relevance for real-world forecasting.

  flowchart TD
    Start["Research Goal: Analyze inflation trends via social media data"] --> Input["Data: 8.5M+ posts from Vkontakte (Jan 2010 - May 2022)"]
    Input --> NLP["NLP Processing: BERT neural networks trained for classification"]
    NLP --> Methodology["Methodology: Identify pro-inflationary vs. disinflationary keywords"]
    Methodology --> SHAP["SHAP Analysis: Visualize key features & context impact"]
    SHAP --> Outcomes["Outcomes: Operational insights on regional inflation expectations"]
    Outcomes --> Scale["Outcome: Scalable approach for other regions (limited by compute cost)"]