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A Framework Model for Integrating Social Media, the Web, and Proprietary Services Into YouTube Video Classification Process
Abstract
Online video streaming has gained ubiquity in disparate educational, governmental, and corporate environments. The ubiquity of these videos elicits new challenges in video classification that is used to promote relative videos and block unwanted content. These challenges include the incorporation of contextual information, rapid development of the ad-hoc query modules, and keeping pace with contemporary contextual information. In this article, the authors present a framework model for incorporating contextual information into video classification information. To illustrate the model, the authors propose a framework which comprises video classification, web search engines, social media platforms, and third-party classification modules. The modules enable the framework's flexibility and adaptivity to different contextual environment—educational, governmental, and corporate. Additionally, the model emphasizes standardized module interface to enable the framework's extensibility and rapid development of future modules.
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