We have collected over 30,000 right- and left-leaning groups’ videos from YouTube, Bitchute, 4Chan and Vimeo to identify aspects of their content and presentation which make these videos more popular and also potentially more persuasive. To date we have collected videos for and against Antifa and other anti-Fascist groups, Black Lives Matter, Proud Boys, Oath Keepers and QAnon and manually labelled subsets for style, stance toward the group, persuasiveness, techniques used and other features. We have also extracted video features including titles, descriptions, time of upload, captions and ASR transcripts, topic categories, and users’ likes, dislikes, comments, and views. We are currently using these to automatically identify information such as the stance of the video (for or against a group), changes in popularity and in the sentiment of viewers toward the videos over time, correlating these changes with major events. We are also extracting text and audio features from videos and their comments to develop multimodal Machine Learning models for use in identifying different types of videos (e.g. pro- and anti- a group, extremely popular or unpopular) and eventually to use in identifying new radical groups and tracking their success. We will also be crowdsourcing surveys of subsets of these videos to understand how persons with different demographics and personality types perceive and are potentially influenced by different groups and different types of videos.