Thousands of news are published everyday reporting worldwide events. Most of these news obtain a low level of popularity and only a small set of events become highly popular in social media platforms. Predicting rare cases of highly popular news is not a trivial task due to shortcomings of standard learning approaches and evaluation metrics. So far, the standard task of predicting the popularity of news items has been tackled by either of two distinct strategies related to the publication time of news. The first strategy, a priori, is focused on predicting the popularity of news upon their publication when related social feedback is unavailable. The second strategy, a posteriori, is focused on predicting the popularity of news using related social feedback. However, both strategies present shortcomings related to data availability and time of prediction. To overcome such shortcomings, we propose a hybrid strategy of time-based ensembles using models from both strategies. Using news data from Google News and popularity data from Twitter, we show that the proposed ensembles significantly improve the early and accurate prediction of rare cases of highly popular news.
Nuno Moniz, Luís Torgo, and Magdalini Eirinaki. "Time-Based Ensembles for Prediction of Rare Events in News Streams" Proceedings of the 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) (2016): 1066-1073. doi:10.1109/ICDMW.2016.0154