J Nn Starsessions Aleksandra 008 Youngtube Vi _verified_ Jun 2026

As the internet continues to evolve, online safety and content moderation have become pressing concerns. Many online platforms have implemented measures to ensure that users can access content safely and responsibly.

Abstract This paper presents a case study applying J‑NN, a convolutional-recurrent neural architecture, to analyze multimodal features in youth-produced video sessions from the StarSessions YoungTube dataset. We process audiovisual and textual metadata from the sample session "Aleksandra_008" to evaluate sentiment, engagement markers, and topical structure. Results show that J‑NN effectively aligns visual attention peaks with linguistic markers of emotional valence and yields a session-level engagement score correlating with platform-derived watch-time (Pearson r = 0.71). We discuss model design, preprocessing pipelines, ethical considerations for minors' data, and directions for scalable analysis. j nn starsessions aleksandra 008 youngtube vi