Minsk, Belarus
Metadata extraction and processing are crucial for social media analysis as they help to systematize information about publications, authors, and audience engagement. The article introduces the web application OmniTrack, designed for automated collection and parameterization of metadata from Russian-language and English-language travel blogs. The application integrates web-scraping methods with a multi-layer architecture and a modular approach, which provides scalability, reproducibility, and extensibility even with changing external platform interfaces. The backend is implemented in Python (Flask); the frontend utilizes HTML, CSS, and JavaScript for an interactive user experience. Data-extraction algorithms are independent modules: undetected_chromedriver for TikTok’s dynamic rendering via browser emulation, yt-dlp for direct JSON-formatted metadata retrieval from YouTube, and Instaloader for high-level access to Instagram’s object model. Collected metadata are normalized to a unified schema in Excel format using the Openpyxl library, which facilitates subsequent statistical analysis. The application underwent usability testing: 42 participants processed 400 posts, evaluating installation simplicity, processing speed, and interface intuitiveness. The mean ease-of-use score was as high as 4.9 out of 5. Some critical issues were identified and resolved, including incompatibility of the pywebview backend module and incorrect handling of shortened TikTok links. The OmniTrack web application provides a robust framework for constructing a representative metadata corpus, supporting further linguistic research into the discursive, genre, and communicative features of Russian-language and English-language travel blogs.
web application, metadata, social-media, web scraping, data automation
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