Comparative Evaluation of Network-Based and NLP-based Methods for Detecting Conspiracy Theory Communities on YouTube

This study explores the effectiveness of network-based community detection methods in detecting misinformation and conspiracy theory communities on YouTube, specifically focusing on earthquake-related conspiracy theories. Given the role of YouTube as a platform for misinformation dissemination, especially during crises like earthquakes, it is essential to identify the communities formed by conspiracy theory propagators. This study compares the performance of network-based methods that leverage viewer behavior data with Natural Language Processing (NLP)-based clustering techniques that analyze video titles. By using the "conspiracy theory score" as an evaluation metric, the study quantitatively evaluates the differences in effectiveness between these methods. The results demonstrate that network-based approaches, particularly Node2Vec, are superior in identifying conspiracy theory communities and high-influence channels, achieving higher Recall and F1 score than NLP-based methods. These findings suggest that network-based methods are highly effective for detecting conspiracy theory communities, even when textual data is limited, offering valuable insights into how conspiracy theories spread within and across communities on YouTube.