arXiv Machine Learning June 16, 2026 By Md. Minhazul Islam, Md. Tanbeer Jubaer, Amith Khandakar, Shovon Sarker, Sumaiya Rahman, Md. Masum Mia, Mohamed Arselene Ayari, Hamed Noori

YTClickbait21K: Human-Annotated Multimodal Dataset for YouTube Clickbait Detection Across Diverse Channels and Content Categories

Read the original on arXiv Machine Learning →

arXiv:2606. 14780v1 Announce Type: cross Abstract: Clickbait content on video-sharing platforms poses a significant challenge to information reliability, yet progress in automated detection has been constrained by the lack of large-scale, high-quality multimodal datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 9

From Content to Audience: A Multimodal Annotation Framework for Broadcast Television Analytics

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

llms nlp multimodal benchmarks

arXiv AI

Jul 24

ClickGuard: Detecting and Spoiling Clickbait News with Informativeness Measures and Large Language Models

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

llms rag nlp

arXiv AI

Aug 25

Let the Bullets Fly: Multimodal Fake News Detection with Temporal-Aligned Generative Danmaku

arXiv:2608.22832v1 Announce Type: new Abstract: The social interactions among crowds via \textit{Danmaku} (a.k.a., bullet comments) on modern multimedia platforms can facilitate both viewpoint confli...

By Xiansheng Luo, Chaowei Zhang, Zewei Zhang, Yi Zhu, Jipeng Qiang

multimodal benchmarks safety

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

llms multimodal safety

arXiv Computer Vision

Sep 15

A parameter-efficient three-branch architecture for multimodal misinformation detection with limited annotations

arXiv:2512.20257v2 Announce Type: replace Abstract: With the rise of easily accessible generative tools for creating and manipulating multimedia content, the threat of realistic synthetic alterations...

By Daniele Cardullo, Simone Teglia, Irene Amerini

llms rag multimodal benchmarks safety

arXiv Computer Vision

Sep 24

MultiVENT-Raw: A Benchmark for Retrieval and Reasoning over Raw Videos

MultiVENT‑Raw is a new multilingual benchmark comprising nearly 120,000 raw videos—continuous footage from cell phones, hand‑held cameras, or CCTV—totaling over 5,300 hours. The dataset includes 130 events and 222 event‑centric queries, along with human‑annotated relevance judgments and extracted key facts for relevant videos. It supports two tasks: retrieving videos relevant to a query event and generating a coherent report summarizing event‑related videos for a target user, with baseline models showing these tasks remain challenging.

By Reno Kriz, David Etter, Alexander Martin, Cameron Carpenter, Debashish Chakraborty, Hannah Recknor, Reihaneh Iranmanesh, Matthew Maciejewski, Kenton Murray, Eugene Yang, Benjamin Van Durme, Aaron Steven White, Andrew Yates, William Walden

multimodal benchmarks

One email a morning, machine-written

Related stories

From Content to Audience: A Multimodal Annotation Framework for Broadcast Television Analytics

ClickGuard: Detecting and Spoiling Clickbait News with Informativeness Measures and Large Language Models

Let the Bullets Fly: Multimodal Fake News Detection with Temporal-Aligned Generative Danmaku

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

A parameter-efficient three-branch architecture for multimodal misinformation detection with limited annotations

MultiVENT-Raw: A Benchmark for Retrieval and Reasoning over Raw Videos