This paper aims to develop a model that accurately detects value-expressing posts on the Russian social media VKontakte. We believe that studying personal values in social media can shed light on how and why social values evolve, especially when stimulus-based methods such as surveys are ineffective (e.g., for hard-to-reach populations). We annotate 5,035 posts using three experts, 304 crowd workers, and ChatGPT, and train several classification models using embeddings from various pre-trained transformer-based language models, applying an ensemble of human and AI-assisted annotations, including an active learning approach. The best performance (F1 = 0.75, F1-macro = 0.80) is achieved using embeddings from the fine-tuned rubert-tiny2 model, which provides an important step forward in studying values within and across Russian social media users. The agreement between crowd workers and experts in post classification is moderate, with ChatGPT showing higher consistency but struggling with spam detection.
