Social Media Virality: Predicting Social Media Cascades over Arbitrary Time Horizons
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In this work, we consider the problem of "information cascades" - i.e., the virality of social network content on social media - and the specific problem of predicting future cascade size over arbitrary time horizons, given information about the content's initial popularity growth. These predictions are useful for a number of applications, including early detection of potentially harmful viral content in online social networks. With application to a large collection of content on Facebook over a two-month period, we predict cascades size using an approach based on Hawkes point process models.