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

Sofiane Abbar; Carlos Castillo; Antonio Sanfilippo

doi:10.1145/3209542.3209575

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

Sofiane Abbar, Carlos Castillo, Antonio Sanfilippo

QEERI - Energy Center

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Citations (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 language	English
Title of host publication	HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media
Publisher	Association for Computing Machinery, Inc
Pages	215-219
Number of pages	5
ISBN (Electronic)	9781450354271
DOIs	https://doi.org/10.1145/3209542.3209575
Publication status	Published - 3 Jul 2018
Event	29th ACM International Conference on Hypertext and Social Media, HT 2018 - Baltimore, United States Duration: 9 Jul 2018 → 12 Jul 2018

Publication series

Name	HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media

Conference

Conference	29th ACM International Conference on Hypertext and Social Media, HT 2018
Country/Territory	United States
City	Baltimore
Period	9/07/18 → 12/07/18

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10.1145/3209542.3209575

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). (HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media). Association for Computing Machinery, Inc. https://doi.org/10.1145/3209542.3209575

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title = "To post or not to post: Using online trends to predict popularity of offline content",

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.",

author = "Sofiane Abbar and Carlos Castillo and Antonio Sanfilippo",

note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 29th ACM International Conference on Hypertext and Social Media, HT 2018 ; Conference date: 09-07-2018 Through 12-07-2018",

year = "2018",

month = jul,

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doi = "10.1145/3209542.3209575",

language = "English",

series = "HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media",

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}

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. HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media, Association for Computing Machinery, Inc, pp. 215-219, 29th ACM International Conference on Hypertext and Social Media, HT 2018, Baltimore, United States, 9/07/18. https://doi.org/10.1145/3209542.3209575

To post or not to post: Using online trends to predict popularity of offline content. / Abbar, Sofiane; Castillo, Carlos; Sanfilippo, Antonio.
HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media. Association for Computing Machinery, Inc, 2018. p. 215-219 (HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Castillo, Carlos

AU - Sanfilippo, Antonio

PY - 2018/7/3

Y1 - 2018/7/3

N2 - 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.

AB - 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.

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M3 - Conference contribution

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BT - HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media

PB - Association for Computing Machinery, Inc

Y2 - 9 July 2018 through 12 July 2018

ER -

Abbar S, Castillo C, Sanfilippo A. 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. Association for Computing Machinery, Inc. 2018. p. 215-219. (HT 2018 - Proceedings of the 29th ACM Conference on Hypertext and Social Media). doi: 10.1145/3209542.3209575

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

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