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The ability to track and monitor relevant and important news in real-time isof crucial interest in multiple industrial sectors. In this work, we focus onthe set of cryptocurrency news, which recently became of emerging interest tothe general and financial audience. In order to track relevant news inreal-time, we (i) match news from the web with tweets from social media, (ii)track their intraday tweet activity and (iii) explore different machinelearning models for predicting the number of the article mentions on Twitterwithin the first 24 hours after its publication. We compare several machinelearning models, such as linear extrapolation, linear and random forestautoregressive models, and a sequence-to-sequence neural network. We find thatthe random forest autoregressive model behaves comparably to more complexmodels in the majority of tasks.

Sensing Social Media Signals for Cryptocurrency News