Abstract
This paper is devoted to solving the problem of determining the age of the author of the text based on models of deep neural networks. The article presents an analysis of methods for determining the age of the author of a text and approaches to determining the age of a user by a photo. This could be a solution to the problem of inaccurate data for training by filtering out incorrect user-specified age data. A detailed description of the author’s technique based on deep neural network models and the interpretation of the results is also presented. The study found that the proposed technique achieved 82% accuracy in determining the age of the author from Russian-language text, which makes it competitive in comparison with approaches for other languages.
Highlights
Text messages are the most popular form of communication and an important part of the daily life of a modern person
As part of the study, the technique for determining the age of the author of a Russian-language text based on the FastText model and the method for filtering user photos using the VGG-Face model was developed
There are no Russian-language corpora to solve this problem, so our own dataset was collected from social networks
Summary
Text messages are the most popular form of communication and an important part of the daily life of a modern person. The features that distinguish the author of a particular message are expressed through the structure of sentences, vocabulary, the use of certain linguistic structures and speech patterns. The obtained solutions of these problems find their application in such important areas of life as information security and forensics, commerce (for example, as a way to optimize targeted advertising), and science (as a tool for linguistic research). They are especially important in forensic expertise, where it is necessary to solve such problems as the following:
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