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