arXiv:2603. 26772v2 Announce Type: replace-cross Abstract: Automated semantic annotation of broadcast television content presents distinctive challenges, combining structured audiovisual composition, domain-specific editorial patterns, and strict operational constraints.
By Paolo Cupini, Francesco Pierri
arXiv:2607. 20463v1 Announce Type: new Abstract: This paper presents an AI-driven browser extension that identifies clickbait to help users avoid misleading Internet articles.
By Wojciech Michaluk, Tymoteusz Urban, Mateusz Kubita, Soveatin Kuntur, Anna Wr\'oblewska
arXiv:2512. 03553v3 Announce Type: replace-cross Abstract: Content moderation remains a critical yet challenging task for large-scale user-generated video platforms, especially in livestreaming environments where moderation must be timely, multimodal, and robust to evolving forms of unwanted content.
By Wei Chee Yew, Hailun Xu, Sanjay Saha, Xiaotian Fan, Hiok Hian Ong, David Yuchen Wang, Kanchan Sarkar, Zhenheng Yang, Danhui Guan
arXiv:2606. 00046v1 Announce Type: cross Abstract: Video platforms such as YouTube have reshaped how users engage with entertainment and information, emphasizing brief, highly engaging content such as Shorts.
By Sydney Johns, Sanjeev Parthasarathy, Shantnu Bhalla, Vaibhav Garg
arXiv:2607. 06875v1 Announce Type: cross Abstract: Understanding and forecasting audience reactions to video content are crucial for improving content creation, recommendation systems, and media analysis.
By Trang Nguyen, Sidong Zhang, Shiv Shankar, Gauri Jagatap, Deepak Chandran, Andrea Fanelli, Madalina Fiterau
Social media videos often communicate meanings that go beyond their visible actions, captions, or speech. A mundane clip may become humorous, ironic, or satire only through the interaction of multimodal cues and cultural context, making such content a difficult test case for video-language models.