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Learning from the News: Predicting Entity Popularity on Twitter

Authors
  • Saleiro, Pedro
  • Soares, Carlos
Type
Preprint
Publication Date
Jul 11, 2016
Submission Date
Jul 11, 2016
Identifiers
arXiv ID: 1607.03057
Source
arXiv
License
Yellow
External links

Abstract

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.

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