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Learning from the News: Predicting Entity Popularity on Twitter

  • Pedro SaleiroEmail author
  • Carlos Soares
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9897)

Abstract

In this work, we tackle the problem of predicting entity popularity on Twitter based on the news cycle. We apply a supervised learning 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.

Keywords

Prediction News Social media Online reputation monitoring 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.DEI-FEUPUniversity of PortoPortoPortugal
  2. 2.LIACCUniversity of PortoPortoPortugal
  3. 3.INESC-TECUniversity of PortoPortoPortugal

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