research-article

Characterizing the life cycle of online news stories using social media reactions

Authors:

Carlos Castillo,

Mohammed El-Haddad,

Jürgen Pfeffer,

Matt StempeckAuthors Info & Claims

CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing

Pages 211 - 223

https://doi.org/10.1145/2531602.2531623

Published: 15 February 2014 Publication History

Abstract

This paper presents a study of the life cycle of news articles posted online. We describe the interplay between website visitation patterns and social media reactions to news content. We show that we can use this hybrid observation method to characterize distinct classes of articles. We also find that social media reactions can help predict future visitation patterns early and accurately. We validate our methods using qualitative analysis as well as quantitative analysis on data from a large international news network, for a set of articles generating more than 3,000,000 visits and 200,000 social media reactions. We show that it is possible to model accurately the overall traffic articles will ultimately receive by observing the first ten to twenty minutes of social media reactions. Achieving the same prediction accuracy with visits alone would require to wait for three hours of data. We also describe significant improvements on the accuracy of the early prediction of shelf-life for news stories.

References

[1]

Agarwal, D., Chen, B. C., and Wang, X. Multi-faceted ranking of news articles using post-read actions. In Proc. of CIKM, ACM (2012), 694--703.

Digital Library

[2]

Ahmed, M., Spagna, S., Huici, F., and Niccolini, S. A peek into the future: predicting the evolution of popularity in user generated content. In Proceedings of the sixth ACM international conference on Web search and data mining (New York, NY, USA, 2013), 607--616.

Digital Library

[3]

Aikat, D. News on the web. Convergence: The International Journal of Research into New Media Technologies 4, 4 (Dec. 1998), 94--110.

[4]

Bandari, R., Asur, S., and Huberman, B. A. The pulse of news in social media: Forecasting popularity. Feb. 2012.

[5]

Bitly Science Team. You just shared a link. how long will people pay attention? The Bit.ly blog, Sept. 2011.

[6]

Bitly Science Team. Halflife by topic. The Bit.ly blog, Nov. 2012.

[7]

Brody, T., Harnad, S., and Carr, L. Earlier web usage statistics as predictors of later citation impact: Research articles. J. Am. Soc. Inf. Sci. Technol. 57, 8 (June 2006), 1060--1072.

Digital Library

[8]

Crane, R., and Sornette, D. Robust dynamic classes revealed by measuring the response function of a social system. PNAS 105, 41 (October 2008), 15649--15653.

[9]

Dezsö, Z., Almaas, E., Lukács, A., Rácz, B., Szakadát, I., and Barabási, A. L. Dynamics of information access on the web. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) 73, 6 (2006), 066132

[10]

.

[11]

Figueiredo, F., Benevenuto, F., and Almeida, J. M. The tube over time: characterizing popularity growth of youtube videos. In Proc. of WSDM, ACM (2011), 745--754.

Digital Library

[12]

Galtung, J., and Ruge, M. H. The structure of foreign news. Journal of Peace Research 2, 1 (1965), 64--91.

[13]

Gharan, S. O., Ronaghi, F., and Wang, Y. What memes say about the news cycle? Tech. rep., Stanford University, 2010.

[14]

Gruhl, D., Guha, R., Kumar, R., Novak, J., and Tomkins, A. The predictive power of online chatter. In Proc. of KDD, ACM (2005), 78--87.

Digital Library

[15]

Hu, Y., John, A., and Seligmann, D. D. Event analytics via social media. In Proc. of SBNMA, ACM (2011), 39--44.

Digital Library

[16]

Huang, S., Chen, M., Luo, B., and Lee, D. Predicting aggregate social activities using Continuous-Time stochastic process. In Proc. of CIKM 2012 (2012).

Digital Library

[17]

Kim, S.-D., Kim, S.-H., and Cho, H.-G. Predicting the virtual temperature of Web-Blog articles as a measurement tool for online popularity. In Computer and Information Technology (CIT), 2011 IEEE 11th International Conference on, IEEE (Aug. 2011), 449--454.

Digital Library

[18]

Lakkaraju, H., and Ajmera, J. Attention prediction on social media brand pages. In Proc. of CIKM, ACM (2011), 2157--2160.

Digital Library

[19]

Lee, J. G., Moon, S., and Salamatian, K. An approach to model and predict the popularity of online contents with explanatory factors. In IEEE Conference on Web Intelligence (2010).

Digital Library

[20]

Lehmann, J., Gonçalves, B., Ramasco, J. J., and Cattuto, C. Dynamical classes of collective attention in twitter. In Proc. of WWW, ACM (2012), 251--260.

Digital Library

[21]

Lerman, K., and Hogg, T. Using a model of social dynamics to predict popularity of news. In Proc. of WWW, ACM (2010), 621--630.

Digital Library

[22]

Lotan, G. Big data for breaking news: Lessons from\#aurora, colorado. Tech. rep., SocialFlow, Aug. 2012.

[23]

Lotan, G., Gaffney, D., and Meyer, C. Audience analysis of major news accounts on twitter. Tech. rep., SocialFlow, Aug. 2011.

[24]

Myers, S. A., Zhu, C., and Leskovec, J. Information diffusion and external influence in networks. In Proc. of KDD, ACM (2012), 33--41.

Digital Library

[25]

Pinto, H., Almeida, J. M., and Gonçalves, M. A. Using early view patterns to predict the popularity of YouTube videos. In Proc. of WSDM, ACM (2013), 365--374.

Digital Library

[26]

Romero, D. M., Meeder, B., and Kleinberg, J. Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and ComplexContagion on twitter. In Proc. of WWW (2011).

Digital Library

[27]

Ruan, Y., Purohit, H., Fuhry, D., Parthasarathy, S., and Sheth, A. Prediction of topic volume on twitter. In WebSci (short papers) (2012).

[28]

Szabo, G., and Huberman, B. A. Predicting the popularity of online content. Communications of the ACM 53, 8 (Aug. 2010), 80--88.

Digital Library

[29]

Tatar, A., Leguay, J., Antoniadis, P., Limbourg, A., de Amorim, M. D., and Fdida, S. Predicting the popularity of online articles based on user comments. In Proc. of WIMS, ACM (2011).

Digital Library

[30]

Wu, F., and Huberman, B. A. Novelty and collective attention. PNAS 104, 45 (Nov. 2007), 17599--17601.

[31]

Yang, J., and Leskovec, J. Patterns of temporal variation in online media. In Proc. of WSDM, ACM (2011), 177--186.

Digital Library

[32]

Yin, P., Luo, P., Wang, M., and Lee, W. C. A straw shows which way the wind blows: ranking potentially popular items from early votes. In Proc. of WSDM, ACM (2012), 623--632.

Digital Library

[33]

Yu, B., Chen, M., and Kwok, L. Toward predicting popularity of social marketing messages. In Social Computing, Behavioral-Cultural Modeling and Prediction, J. Salerno, S. J. Yang, D. Nau, and S. K. Chai, Eds., vol. 6589 of LNCS, Springer (2011), 317--324.

Digital Library

Cited By

Lin STulabandu SKoch JHayes RBarnes APurohit HChen SHan BXue H(2024)Public Response to Federal Electronic Cigarette Regulations Analyzed Using Social Media Data Through Natural Language Processing: Topic Modeling StudyJournal of Medical Internet Research10.2196/5891926(e58919)Online publication date: 1-Oct-2024
https://doi.org/10.2196/58919
Yan ZLi YHuang LJi W(2024)Fake Social Media News Detection Based on Forwarding User RepresentationIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333144611:3(3432-3443)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3331446
Trilling DDubèl RKiddle RKroon ALin ZSimon MVermeer SWelbers KBoukes M(2024)What is Popular Gets More Popular? Exploring Over-Time Dynamics in Article Readership Using Real-World Log DataJournalism Studies10.1080/1461670X.2024.2411334(1-21)Online publication date: 14-Oct-2024
https://doi.org/10.1080/1461670X.2024.2411334
Show More Cited By

Index Terms

Characterizing the life cycle of online news stories using social media reactions
1. Information systems
  1. Information systems applications

Recommendations

Linking online news and social media
WSDM '11: Proceedings of the fourth ACM international conference on Web search and data mining

Much of what is discussed in social media is inspired by events in the news and, vice versa, social media provide us with a handle on the impact of news events. We address the following linking task: given a news article, find social media utterances ...
Social Media Marketing: A Beginner Guide To Get Success In Your Business (Volume 1)
News media, social media, and hyperlink networks: An examination of integrated media effects

Research typically focuses on one medium. But in today's digital media environment, people use and are influenced by their experience with multiple systems. Building on media ecology research, we introduce the notion of integrated media effects. We draw ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CSCW '14: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing

February 2014

1600 pages

ISBN:9781450325400

DOI:10.1145/2531602

General Chairs:
Susan Fussell
Cornell University
,
Wayne Lutters
University of Maryland, Baltimore County
,
Program Chairs:
Meredith Ringel Morris
Microsoft Research
,
Madhu Reddy
Penn State University

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 February 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CSCW'14

Sponsor:

SIGCHI

CSCW'14: Computer Supported Cooperative Work

February 15 - 19, 2014

Maryland, Baltimore, USA

Acceptance Rates

CSCW '14 Paper Acceptance Rate 134 of 497 submissions, 27%;

Overall Acceptance Rate 2,235 of 8,521 submissions, 26%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

109
Total Citations
View Citations
1,549
Total Downloads

Downloads (Last 12 months)120
Downloads (Last 6 weeks)12

Reflects downloads up to 14 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lin STulabandu SKoch JHayes RBarnes APurohit HChen SHan BXue H(2024)Public Response to Federal Electronic Cigarette Regulations Analyzed Using Social Media Data Through Natural Language Processing: Topic Modeling StudyJournal of Medical Internet Research10.2196/5891926(e58919)Online publication date: 1-Oct-2024
https://doi.org/10.2196/58919
Yan ZLi YHuang LJi W(2024)Fake Social Media News Detection Based on Forwarding User RepresentationIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333144611:3(3432-3443)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3331446
Trilling DDubèl RKiddle RKroon ALin ZSimon MVermeer SWelbers KBoukes M(2024)What is Popular Gets More Popular? Exploring Over-Time Dynamics in Article Readership Using Real-World Log DataJournalism Studies10.1080/1461670X.2024.2411334(1-21)Online publication date: 14-Oct-2024
https://doi.org/10.1080/1461670X.2024.2411334
Tao YBoukes MSchuck A(2024)Unpacking the Nuances of Agenda-Setting in the Online Media Environment: An Hourly-Event Approach in the Context of Chinese Economic NewsJournalism Studies10.1080/1461670X.2024.2345681(1-20)Online publication date: 6-May-2024
https://doi.org/10.1080/1461670X.2024.2345681
Brannon WRoy D(2024)The speed of news in Twitter (X) versus radioScientific Reports10.1038/s41598-024-61921-714:1Online publication date: 24-May-2024
https://doi.org/10.1038/s41598-024-61921-7
Kowal M(2023)The Value of a Like: Facebook, Viral Posts, and Campaign Finance in US Congressional ElectionsMedia and Communication10.17645/mac.v11i3.666111:3(153-163)Online publication date: 3-Aug-2023
https://doi.org/10.17645/mac.v11i3.6661
Luo YShi YLi S(2023)Social media fake news detection algorithm based on multiple feature groups2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA)10.1109/ICIBA56860.2023.10165545(91-95)Online publication date: 26-May-2023
https://doi.org/10.1109/ICIBA56860.2023.10165545
Reddy BSiva Kumar A(2023)Multimodal Approaches based on Fake News Detection2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS)10.1109/ICAIS56108.2023.10073839(751-755)Online publication date: 2-Feb-2023
https://doi.org/10.1109/ICAIS56108.2023.10073839
Chakraborty RChakraborty N(2023)TwMiner: Mining Relevant Tweets of News Articles2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)10.1109/CCGridW59191.2023.00052(1-3)Online publication date: May-2023
https://doi.org/10.1109/CCGridW59191.2023.00052
Boling KWalsh J(2023)“We Hold that Roe and Casey Must Be Overruled.” #scotus: Digital Journalism on Abortion RightsJournalism Practice10.1080/17512786.2023.2298239(1-18)Online publication date: 26-Dec-2023
https://doi.org/10.1080/17512786.2023.2298239
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents