Documentation

VideoTracker is a comprehensive platform developed under the Cosmos Research Lab at UALR to help researchers, analysts, and developers manage and study large volumes of YouTube content data. The platform allows you to collect, categorize, and visualize video and channel data from YouTube in a structured way. It includes powerful analytics tools to track trends, sentiment analysis, narrative detection, topic distribution, posting frequency, and engagement patterns across different videos and channels over time, making it an essential tool for social media research and academic projects.

Trackers in VideoTracker allow you to organize and monitor specific YouTube videos and channels. You can create a new tracker from the tracker page, add videos or channels from explore page and manage them through the tracker management interface. Each tracker provides its own dashboard with analytics.

VideoTracker provides a comprehensive suite of analytics including sentiment analysis, emotional tone, topic distribution analysis to identify key themes, posting frequency charts to track content publication patterns, word cloud analysis for keyword extraction, narratives, clusters, engagement metrics, Youtube Channel Characterization, suspicious score, content trap networks, and symbol analysis (SCEP). The dashboard also includes daily video views charts, related videos network graphs, location mapping, language distribution, and social media footprint analysis to give you a complete picture of your tracked content.

VideoTracker includes built-in sentiment analysis that evaluates the emotional tone of video content. You can access these analyses through the dashboard's sentiment analysis section, which provide visualizations and detailed breakdowns. The system processes video titles, descriptions, and transcripts to generate these insights, helping researchers understand content characteristics.

VideoTracker allows you to monitor and analyze a wide range of YouTube content including individual videos, entire channels and trending content.

VideoTracker was designed with research in mind. It provides tools to structure large YouTube datasets, perform statistical and text-based analyses, and visualize findings. Academics can use it to study trends in online video content, social media influence, misinformation spread, audience engagement patterns, and content diffusion. The platform supports detailed analytics enabling researchers to conduct comprehensive studies on YouTube content and its impact.

Data collected from YouTube is handled responsibly, respecting public content policies and YouTube's terms of service. User accounts are protected, and your tracked content is organized per user, ensuring that your research data remains private. The platform follows best practices for data security and privacy compliance.

Yes, VideoTracker is optimized for scalability. It uses efficient data storage, indexing, and caching strategies to handle large volumes of YouTube video and channel data without performance degradation. Users can process, filter, and analyze datasets with thousands of videos and channels, perform batch analytics, and generate comprehensive reports. The platform's architecture ensures responsive performance even as your tracked content grows, making it suitable for both small projects and large-scale research studies involving extensive YouTube content analysis.