Generative Neural Networks in Empirical Sociological Research
Opportunities and Limitations
Bibliographic Data
| ID | 12998295 |
|---|---|
| Authors | V A Smirnov (0000-0001-7130-2194, Lomonosov Moscow State University, corresponding author), Vladimir Smirnov (0000-0001-6481-1281, Lomonosov Moscow State University) |
| Year | 2025 |
| Volume | 19 |
| Issue | 3 |
| Pages | 52-62 |
| Publication date | 2025-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Ойкумена Регионоведческие исследования (JOURNAL) |
| Journal identifiers | ISSN: 1998-6785 |
| Publisher | Federal State Budgetary Educational Institution of Higher Education Vladivostok State University (PUBLISHER) |
| DOI | 10.63973/1998-6785/2025-3/52-62 |
| OpenAlex | W4414735322 |
| Language | EN |
| References cited | 17 |
The article analyzes the use of artificial intelligence algorithms in the professional practices of empirical sociologists, in particular, it explores the possibilities and limitations of using generative neural networks for developing research program, processing, and analyzing data. The article proposes a typology of neural networks used in research and data analysis, and highlights key practices for their use. It also provides insights into the specific possibilities and limitations of incorporating generative neural networks into the professional practices of empirical sociologists
Computational and Text Analysis Methods · Opinion Dynamics and Social Influence
| Citation velocity | historical |
|---|---|
| Highly cited | No |