Learning from the News: Predicting Entity Popularity on Twitter

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Saleiro, P (Author) Other; Carlos Soares (Author) FEUP

Conference proceedings International

Title: ADVANCES IN INTELLIGENT DATA ANALYSIS XV

Pages: 171-182

15th International Symposium on Intelligent Data Analysis (IDA)

Stockholm Univ, Dept Comp & Syst Sci, Stockholm, SWEDEN, OCT 13-15, 2016

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 13 Citations

Other information

Authenticus ID: P-00K-PM1

DOI: 10.1007/978-3-319-46349-0_15

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12