1 citations · 1 across the 5 of their papers we have counts for
5 papers
MetaHarm: Harmful YouTube Video Dataset Annotated by Domain Experts, GPT-4-Turbo, and Crowdworkers
Wonjeong Jo, Magdalena Wojcieszak
Short video platforms, such as YouTube, Instagram, or TikTok, are used by billions of users. These platforms expose users to harmful content, ranging from clickbait or physical har…
"Whose Side Are You On?" Estimating Ideology of Political and News Content Using Large Language Models and Few-shot Demonstration Selection
Muhammad Haroon, Magdalena Wojcieszak, Anshuman Chhabra
The rapid growth of social media platforms has led to concerns about radicalization, filter bubbles, and content bias. Existing approaches to classifying ideology are limited in th…
Re-ranking Using Large Language Models for Mitigating Exposure to Harmful Content on Social Media Platforms
Rajvardhan Oak, Muhammad Haroon, Claire Jo +2
Social media platforms utilize Machine Learning (ML) and Artificial Intelligence (AI) powered recommendation algorithms to maximize user engagement, which can result in inadvertent…
Towards Safer Social Media Platforms: Scalable and Performant Few-Shot Harmful Content Moderation Using Large Language Models
Akash Bonagiri, Lucen Li, Rajvardhan Oak +3
The prevalence of harmful content on social media platforms poses significant risks to users and society, necessitating more effective and scalable content moderation strategies. C…
Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators
Claire Wonjeong Jo, Miki Wesołowska, Magdalena Wojcieszak
Short video platforms, such as YouTube, Instagram, or TikTok, are used by billions of users globally. These platforms expose users to harmful content, ranging from clickbait or phy…