arXiv AI July 9, 2026 By Paolo Cupini, Francesco Pierri

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.

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 AI.

arXiv Machine Learning

Jun 16

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

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.

By Md. Minhazul Islam, Md. Tanbeer Jubaer, Amith Khandakar, Shovon Sarker, Sumaiya Rahman, Md. Masum Mia, Mohamed Arselene Ayari, Hamed Noori

multimodal benchmarks

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 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 9

Bridging Traditional Explainability Methods and Multimodal Multilingual Models: An XAI-Based Analysis

arXiv:2606. 07533v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) effectively integrate text and audio to interpret context in complex interactive dialogues.

By Pawe{\l} Pozorski, Jakub Muszy\'nski, Maria Ganzha

llms nlp multimodal safety

arXiv AI

2d ago

SONIC-O1: A Real-World Benchmark for Evaluating Multimodal Large Language Models on Audio-Video Understanding

SONIC‑O1 is a new benchmark designed to evaluate multimodal large language models on audio‑video understanding. It contains 60 hours of 231 clips across 13 real‑world conversational domains, with 4,958 human‑verified annotations and demographic metadata. The benchmark tests open‑ended summarization, multiple‑choice question answering, and temporally grounded reasoning, revealing performance gaps between model families and across demographic groups.

By Ahmed Y. Radwan, Christos Emmanouilidis, Hina Tabassum, Deval Pandya, Shaina Raza

llms nlp multimodal benchmarks

arXiv Machine Learning

Jul 9

Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild

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

llms agents fine-tuning multimodal benchmarks

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Related stories

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

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

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

Bridging Traditional Explainability Methods and Multimodal Multilingual Models: An XAI-Based Analysis

SONIC-O1: A Real-World Benchmark for Evaluating Multimodal Large Language Models on Audio-Video Understanding

Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild