Evaluating AI capabilities in detecting conspiracy theories on YouTube

TL;DR

This paper evaluates the effectiveness of large language models in detecting conspiracy theory videos on YouTube, highlighting their strengths in recall and limitations in precision, with implications for online content moderation.

Contribution

It compares open-weight LLMs and a fine-tuned RoBERTa baseline for conspiracy detection, providing insights into their performance and limitations in real-world scenarios.

Findings

Text-based LLMs have high recall but lower precision.

Multimodal models show limited benefits over text-only models.

RoBERTa performs nearly as well as larger LLMs in real-world tests.

Abstract

As a leading online platform with a vast global audience, YouTube's extensive reach also makes it susceptible to hosting harmful content, including disinformation and conspiracy theories. This study explores the use of open-weight Large Language Models (LLMs), both text-only and multimodal, for identifying conspiracy theory videos shared on YouTube. Leveraging a labeled dataset of thousands of videos, we evaluate a variety of LLMs in a zero-shot setting and compare their performance to a fine-tuned RoBERTa baseline. Results show that text-based LLMs achieve high recall but lower precision, leading to increased false positives. Multimodal models lag behind their text-only counterparts, indicating limited benefits from visual data integration. To assess real-world applicability, we evaluate the most accurate models on an unlabeled dataset, finding that RoBERTa achieves performance close to LLMs with a larger number of parameters. Our work highlights the strengths and limitations of current LLM-based approaches for online harmful content detection, emphasizing the need for more precise and robust systems.