Prediction of the popularity of online textual snippets gained much attention in recent years. In this paper we investigate some of the factors that contribute to popularity of specific phrases such as Twitter hashtags. We define a new prediction task and propose a linguistically motivated algorithm for accurate prediction of hashtag popularity. Our prediction algorithm successfully models the interplay between various constraints such as the length restriction, typing effort and ease of comprehension. Controlling for network structure and social aspects we get a glimpse into the processes that shape the way we produce language and coin new words. In order to learn the interactions between the constraints we cast the problem as a ranking task. We adapt Gradient Boosted Trees for learning ranking functions in order to predict the hashtags/neologisms to be accepted. Our results outperform several baseline algorithms including SVM-rank, while maintaining higher interpretability, thus our model's prediction power can be used for better crafting of future hashtags.
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