Abstract
These days it is easy to create and share content online. Millions of people create and share their online content, that is consumed by millions more, daily. This flow of content and consumption has been used as a channel for disseminating digital advertisements, generating publicity for brands and financial return for content creators. Thus, identifying whether a video will be popular in the first moments after its publication is of great value to advertisers. Using Random Forest, we classify Facebook videos as popular or unpopular based on their number of views, using early views and visual features extracted from the videos as predictor features. Our results indicate that using the combination of early views with visual features yields the best results, allowing the prediction of popularity to be made as early as possible.
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