Cyber-threat intelligence (CTI) has matured and grown into its own industry within recent years. Many CTI efforts involve scrutinizing text-based conversations in DarkNet forums and markets. However, hackers commonly share knowledge and other information through video formats that have been largely ignored. Further, cybercriminals are increasingly making use of mainstream social media to transmit hacking knowledge and assets, but this has gone unexplored in literature. In this research-in-progress, a video classifier to detect cybercriminal content in mainstream social media is designed and implemented. A collection of hacking and non-hacking videos was retrieved from a popular social media website to serve as a testbed. Feature sets included video metadata as well as features engineered from the videos themselves, including object detection and aesthetic qualities. This study demonstrates a methodological proof-of-concept that can enable future research that further investigates cyber-adversarial video contents, which have remained largely unexplored to this day. This study also contributes to literature regarding cyber-adversarial contents in mainstream social media.