The article explores the “digital traces” of the structural integration of cross-border migrants in Russia by combining qualitative text analysis methods with automatic classifiers. Our goal was to analyze posts related to job and housing searches in migrant communities on VKontakte in order to describe the challenges these individuals face and how they use social media to overcome them. From the wide range of practices falling under the definition of structural integration, we focused on job and housing searches, considering them the most significant. Our tasks were to identify and classify mentions of these practices in migrant groups on VK, determining the role of social media in these processes. Given the large volume of material, achieving these goals required an approach that combined the capabilities of automatic classifiers with the analytical sensitivity of qualitative methods. The use of a linear classifier did not yield the expected results, prompting the need to train a neural network classifier. To reduce the associated labor costs, we tested the possibility of training the model on a small manually selected dataset of short texts. Using a Python-based parser, we extracted 129,261 posts from migrant groups, categorizing them thematically via web interface. The texts were then used to train a BERT network (F1 score: 0.94). To ensure the accuracy of classification and to obtain preliminary sociological insights, a randomized sample was analyzed using qualitative content analysis. This analysis revealed that the digital platforms under study serve as spaces where migrants build “the strength of weak ties,” accumulating information and social capital, which in turn reduces the costs and risks associated with integration when searching for jobs and housing in resource-scarce conditions. Religious and regional solidarity serve as the foundation of these networks. Additionally, digital media platforms become contact points between migrants and the host society: they facilitate the informal involvement of migrants, especially refugees from Ukraine, Donetsk, and Luhansk, into family economies and caregiving roles. For example, migrants are offered low-paying agricultural work or caregiving jobs for elderly relatives in small towns or villages, with free accommodation provided by the employer. Another category of posts offers housing in Russian cities to refugee women in exchange for domestic labor and sex. This indicates that some migrant groups may be part of the broader problem of sexual exploitation of female refugees in Russia and the marginalization of migrants by the host community. The results are consistent with those obtained through more traditional methods, indicating that this approach can be applied to the study of large datasets generated by less-researched communities.