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A Holistic Approach to Undesired Content Detection in the Real World

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We present a holistic approach to building a robust and useful natural language classification system for real-world content moderation.

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arXiv AI
Jun 4

Dynamic Content Moderation in Livestreams: Combining Supervised Classification with MLLM-Boosted Similarity Matching

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 Machine Learning
Aug 4

Network Information Enhances Unreliable News Domain Detection

arXiv:2608. 02399v1 Announce Type: cross Abstract: Content-based detection of unreliable news is increasingly difficult, as low-reliability sources mimic credible journalism and generative AI makes fabricated content harder to flag.

By Raphaela Ke{\ss}ler, Roman David Ventzke, Viola Priesemann, Giordano De Marzo