Predicting the age of social network users from user-generated texts with word embeddings

Many web-based applications such as advertising or recommender systems often critically depend on the demographic information, which may be unavailable for new or anonymous users. We study the problem of predicting demographic information based on user-generated texts on a Russian-language dataset from a large social network. We evaluate the efficiency of age prediction algorithms based on word2vec word embeddings and conduct a comprehensive experimental evaluation, comparing these algorithms with each other and with classical baseline approaches.