# Learning from the News: Predicting Entity Popularity on Twitter

arXiv: https://arxiv.org/abs/1607.03057 · HTML: https://mlsanity.com/paper/1607.03057/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2016.

## Built on by (most cited first, 1 of 1)

- [Social-Media Activity Forecasting with Exogenous Information Signals](https://mlsanity.com/paper/2109.11024/) (2021)

## Abstract (arXiv)

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.