Abstract
We investigate the problem of early prediction of item popularity in online social networks. Prior work claims that the time taken by each item to reach i adopters (i being a small number around 5) has a higher predictive power than other non-temporal features, such as those related to the characteristics of the adopters. Here, we challenge this claim by proposing a new feature, based on the users’ intuitions, which is shown to provide significantly better predictive power for the most popular items than the above-mentioned temporal feature. A GoodReads dataset is used to illustrate the merits of the proposed method.
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