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.

翻译：通胀是对任何国家和地区人口均具有重大影响的最重要宏观经济指标之一。通胀受多种因素影响，其中通胀预期是重要因素之一。许多央行在通胀目标制框架下实施货币政策时会考虑这一因素。当前，大量人群是互联网尤其是社交网络的活跃用户。存在一种假设：人们主要搜索、阅读和讨论那些自身特别关注的话题。逻辑上可以推测，价格动态也可能成为用户讨论的焦点。因此，此类讨论可被视为获取通胀预期信息的更快速替代来源。本研究基于Vkontakte社交网络非结构化数据，分析通胀上升与下降趋势（以鄂木斯克州为例）。研究收集了2010年1月至2022年5月间超过850万条帖子样本。研究者采用BERT神经网络解决问题，该模型相较基准模型（如逻辑回归、决策树分类器等）展现出更优效果。这使得我们能够定义不同语境下的促通胀与抑通胀关键词类型，并通过SHAP方法实现可视化。该分析提供了关于区域层面通胀过程的补充操作信息，所提方法可推广至其他地区。但本研究的局限性在于，需为俄罗斯所有地区初始训练类似模型所需的时间与算力成本。