Abstract
Understanding the spread of information in complex networks is a key problem. Content sharing in popular online social networks such as Facebook and Twitter has been well studied, however, the future trajectory of a cascade has been shown to be inherently unpredictable. Nonetheless, cascade virality has recently been studied as a classification problem, resulting in good prediction accuracy. Herein, we address the important problem of pirated media popularity estimation in torrent applications, such as Project Free TV, Popcorn-Time, and The Pirate Bay. Although pirating software and media is illegal, the practice of pirating is actually growing in popularity. On a large sample of data acquired from The Pirate Bay, we demonstrate high accuracy in the task of identifying whether the popularity of a torrent will continue to grow in the future. Specifically, we achieve close to perfect accuracy in estimating the order-of-magnitude popularity of torrents.
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Chelmis, C., Zois, DS. (2018). Order-of-Magnitude Popularity Estimation of Pirated Content. In: Özyer, T., Alhajj, R. (eds) Machine Learning Techniques for Online Social Networks. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-89932-9_5
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