  In this work, we tackle the problem of predicting entity popularity on
Twitter based on the news cycle. We apply a supervised learn- ing approach and
extract four types of features: (i) signal, (ii) textual, (iii) sentiment and
(iv) semantic, which we use to predict whether the popularity of a given entity
will be high or low in the following hours. We run several experiments on six
different entities in a dataset of over 150M tweets and 5M news and obtained F1
scores over 0.70. Error analysis indicates that news perform better on
predicting entity popularity on Twitter when they are the primary information
source of the event, in opposition to events such as live TV broadcasts,
political debates or football matches.
