Advancing video categorization

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Vubble is a media tech company that builds solutions for trustworthy digital video distribution and curation. Using a combination of**algorithms and human curators, Vubble searches the internet to locate video content of interest to its users. Vubble has**experienced significant year over year growth since incorporation, with an expanding customer base in the education and media**spaces. They have spent several years building their technology and confirming their product market fit with early customers and**are ready to scale. Through this project, Vubble with collaborate with Dr. Vida Movahedi from Senecas School of Information and**Communications Technology to develop a machine-learning algorithm that will automatically output highly probable categories for**videos. With this algorithm implemented into the Vubble workflow to assist in automated video identification, Vubble will be able to**better address their existing, and emerging, customer demands, while increasing their productivity and competitiveness. This**video identification research project will be Vubbles first step in understanding how to automate the identification of accurate**video. The need for automation of videos curation is prevalent, as video is quickly becoming the worlds dominant form of media**consumption, particularly for digital-native younger audiences. Furthermore, the results of the applied research will aid Vubble in**moving forward in addressing what they believe is a looming problem facing all media consumers, and society, the rising of fake**news video created from archival footage.