To post or not to post: Using online trends to predict popularity of offline content

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Predicting the popularity of online content has attracted much attention in the past few years. In news rooms, journalists and editors are keen to know, as soon as possible, the articles that will bring the most traffic into their website. In this paper, we propose a new approach for predicting the popularity of news articles before they go online. Our approach complements existing content-based methods, and is based on a number of observations regarding article similarity and topicality. We use time series forecasting to predict the number of visits an article will receive. Our experiments on real data collections demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationHT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media
PublisherAssociation for Computing Machinery, Inc
Pages215-219
Number of pages5
ISBN (Electronic)9781450354271
DOIs
Publication statusPublished - 3 Jul 2018
Event29th ACM International Conference on Hypertext and Social Media, HT 2018 - Baltimore, United States
Duration: 9 Jul 201812 Jul 2018

Other

Other29th ACM International Conference on Hypertext and Social Media, HT 2018
CountryUnited States
CityBaltimore
Period9/7/1812/7/18

    Fingerprint

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

Cite this

Abbar, S., Castillo, C., & Sanfilippo, A. (2018). To post or not to post: Using online trends to predict popularity of offline content. In HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media (pp. 215-219). Association for Computing Machinery, Inc. https://doi.org/10.1145/3209542.3209575