Learning from the News: Predicting Entity Popularity on Twitter

TL;DR

This paper presents a supervised learning method that predicts Twitter entity popularity using news-based features, achieving over 0.70 F1 score across multiple entities and highlighting the importance of news as an information source.

Contribution

It introduces a novel feature set combining signal, textual, sentiment, and semantic data for predicting entity popularity on Twitter.

Findings

News-based features improve prediction accuracy.

Prediction performance varies with event type.

Over 0.70 F1 score achieved on multiple entities.

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.