research-article

Lifespan and propagation of information in On-line Social Networks

Authors:

Giannis Haralabopoulos,

Ioannis Anagnostopoulos,

Sherali ZeadallyAuthors Info & Claims

Journal of Network and Computer Applications, Volume 56, Issue C

Pages 88 - 100

https://doi.org/10.1016/j.jnca.2015.06.006

Published: 01 October 2015 Publication History

Abstract

Since 1950, information flows have been in the center of scientific research. Up until the Internet penetration in the late 1990s, these information flow studies were based on traditional offline social networks. From the first Online Social Network studies, various observations of "offline" information flows, such as the two-step flow of communication and the importance of weak ties, were verified in several "online" studies, also indicating that information flows from one Online Social Network (OSN) to several others. Within that flow, information is shared with and reproduced, by users of each network. Furthermore, the original content is enhanced or weakened according to its topic, as well as the dynamic nature and exposure of each Online Social Networks (OSNs). In such an informational connected environment, each OSN is considered as a layer of information flows, which interacts with other layers. We examine information flows in several social networks, as well as their diffusion and lifespan, across these networks, based on user-generated content. Our results verify the information connection in various OSNs and provide a measurement of shared information lifetime in multiple OSNs.

References

[1]

D.T. Allsop, B.R. Bassett, J.A. Hoskins, Word-of-mouth research: principles and applications, J Advert Res, 47 (2007) 398.

[2]

Bakshy E, Itamar R, Cameron M, Adamic L. The role of social networks in information diffusion. In: Proceedings of the 21st international conference on world wide web; 2012. p. 519-28.

[3]

Berger J, Milkman K. What makes online content viral? Wharton research paper; 2010.

[4]

F. Bonchi, Influence propagation in social networks: a data mining perspective, IEEE Intell Inform Bull, 12 (2011) 8-16.

[5]

Goyal A, Bonchi F, Lakshmanan LVS. Learning influence probabilities in social networks. In: Proceedings of the 3rd ACM international conference on web search and data mining; 2010. p. 241-50.

[6]

Granovetter, S. Mark, The strength of weak ties, Am J Sociol (1973) 1360-1380.

[7]

Guerini M, Strapparava C, Özbal G. Exploring text virality in social networks. In: Proceedings of ICWSM; 2011.

[8]

Guerini M, Pepe A, Lepri B. Do linguistic style and readability of scientific abstracts affect their virality? Proceedings of the sixth international conference on weblogs and social media; 2012.

[9]

Guille A, Hacid H. A predictive model for the temporal dynamics of information diffusion in online social networks. In: Proceedings of the 21st international conference companion on world wide web; 2012. p. 1145-52.

[10]

Hansen LK, Arvidsson A, Nielsen FÅ, Colleoni E, Etter M., Good friends, bad news-affect and virality in twitter, future information technology; 2011. p. 34-43.

[11]

Ienco D, Bonchi F, Castillo C., The meme ranking problem: maximizing microblogging virality, data mining workshops; 2010. p. 328-35.

[12]

J.L. Iribarren, E. Moro, Affinity paths and information diffusion in social networks, Soc Netw, 33 (2011) 134-142.

[13]

S. Jurvetson, and T. Draper, Viral marketing: viral marketing phenomenon explained, DFJ Netw News, 1997

[14]

A. Karnik, A. Saroop, V. Borkar, On the diffusion of messages in on-line social networks, Perform Eval, 70 (2013) 271-285.

Digital Library

[15]

E. Katz, P.F. Lazarsfeld, Personal influence, the part played by people in the flow of mass communications, Transaction Publishers, 1970.

[16]

Kwak H, Lee C, Park H, Moon S. What is twitter, a social network or a news media? Proceedings of the 19th international conference on world wide web; 2010. p. 591-600.

Digital Library

[17]

P.F. Lazarsfeld, B. Berelson, H. Gaudet, The people's choice: how the voter makes up his mind in a presidential campaign, Columbia University Press, 1944.

[18]

Leskovec J, Krause A, Guestrin C, Faloutsos C, VanBriesen J, Glance N. Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining; 2007. p. 420-29.

Digital Library

[19]

Li Y, Huang S, Fan C, Yang G. The selection of information diffusion monitoring nodes in directed online social networks. In: Proceedings of the 2012 international conference on information technology and software engineering; 2013. p. 533-40.

[20]

N. Long, Development sociology: actor perspectives, Routledge, 2001.

[21]

Mathioudakis M, Bonchi F, Castillo C, Gionis A, Ukkonen A. Sparsification of Influence networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining; 2011. p. 529-37.

[22]

Nahon K, Hemsley J, Walker S, Hussain M. Blogs: spinning a web of virality, Proceedings of the iConference; 2011. p. 348-55.

[23]

L. Porter, G.J. Golan, From subservient Chickens to Brawny Men: a comparison of viral advertising to television advertising, J Interact Advert, 6 (2006) 30-38.

[24]

Rajyalakshmi S, Amitabha B, Soham D, Tripathy RM. Topic diffusion and emergence of virality in social networks; 2012. {arXiv:1202.2215}.

[25]

Rodriguez MG, Leskovec J, Krause A. Inferring networks of diffusion and influence. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining; 2010. p. 1019-28.

[26]

Romero DM, Meeder B, Kleinberg J. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th international conference on world wide web; 2011. p. 695-704.

[27]

D.M. Romero, W. Galuba, S. Asur, B.A. Huberman, Influence and passivity in social media, Mach Learn Knowl Discov.Databases (2011) 18-33.

[28]

Sakaki T, Okazaki M, Matsuo Y. Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th international conference on world wide web; 2010. p. 851-60.

Digital Library

[29]

Tucker C. The reach and persuasiveness of viral video ads. Available at SSRN 1952746; 2011.

[30]

Weng L, Ratkiewicz J, Perra N, Goncalves B, Castillo C, Bonchi F, Schifanella R, Menczer F, Flammini A., The role of information diffusion in the evolution of social networks. In: Proceedings of the 19th ACM SIGKDD conference on knowledge discovery and data mining; 2013.

Digital Library

[31]

Yang J, Counts S. Predicting the speed, scale, and range of information diffusion in twitter. In: Proceedings of the ICWSM; 2010. p. 355-8.

[32]

Yang J, Leskovec J. Patterns of temporal variation in online media. In: Proceedings of the fourth ACM international conference on web search and data mining; 2011. p. 177-86.

Cited By

Kairam SBernstein MBruckman AChancellor SChandrasekharan EDe Choudhury MFiesler CLi HProferes NHorta Ribeiro MSmith CWeld GFarzan RLópez CCardoso Llach DQuercia DMustafa MNiu SWong-Villacrés M(2024)Community-Driven Models for Research on Social PlatformsCompanion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing10.1145/3678884.3687141(684-688)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1145/3678884.3687141
Poux-Médard GVelcin JLoudcher S(2023)Multivariate Powered Dirichlet-Hawkes ProcessAdvances in Information Retrieval10.1007/978-3-031-28238-6_4(47-61)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-28238-6_4
Razis GAnagnostopoulos IZeadally S(2020)Modeling Influence with Semantics in Social NetworksACM Computing Surveys10.1145/336978053:1(1-38)Online publication date: 6-Feb-2020
https://dl.acm.org/doi/10.1145/3369780
Show More Cited By

Recommendations

Does social capital affect SNS usage? A look at the roles of subjective well-being and social identity

Examines the effects of social capital on SNS use (qualitative and quantitative use).Found that bridging capital significantly impacts qualitative SNS use.Verified the mediating role of subjective well-being between bonding capital and quantitative SNS ...
SNS flow, SNS self-disclosure and post hoc interpersonal relations change: Focused on Korean Facebook user
Highlights
- The SNS flow experiences of intrinsic motivation have impact on the post hoc interpersonal relations.
- The SNS flow experiences of intrinsic motivation have impact on the SNS self-disclosure.
- The SNS self-disclosure has impact on ...
Abstract
This study empirically investigates how the flow experiences when using SNS have impacted SNS interaction and interpersonal relationships. Using a survey of Facebook users, this study examines how the SNS flow as an intrinsic motivation have an ...
Practices of balancing privacy and publicness in social network services
GROUP '10: Proceedings of the 2010 ACM International Conference on Supporting Group Work

While social media is all about sharing content with a community, few people wish to share everything, with everyone, all the time. This means that users balance between making some things public and keeping other content private. The presented ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Journal of Network and Computer Applications

Journal of Network and Computer Applications Volume 56, Issue C

October 2015

205 pages

ISSN:1084-8045

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Academic Press Ltd.

United Kingdom

Publication History

Published: 01 October 2015

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Kairam SBernstein MBruckman AChancellor SChandrasekharan EDe Choudhury MFiesler CLi HProferes NHorta Ribeiro MSmith CWeld GFarzan RLópez CCardoso Llach DQuercia DMustafa MNiu SWong-Villacrés M(2024)Community-Driven Models for Research on Social PlatformsCompanion Publication of the 2024 Conference on Computer-Supported Cooperative Work and Social Computing10.1145/3678884.3687141(684-688)Online publication date: 11-Nov-2024
https://dl.acm.org/doi/10.1145/3678884.3687141
Poux-Médard GVelcin JLoudcher S(2023)Multivariate Powered Dirichlet-Hawkes ProcessAdvances in Information Retrieval10.1007/978-3-031-28238-6_4(47-61)Online publication date: 2-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-28238-6_4
Razis GAnagnostopoulos IZeadally S(2020)Modeling Influence with Semantics in Social NetworksACM Computing Surveys10.1145/336978053:1(1-38)Online publication date: 6-Feb-2020
https://dl.acm.org/doi/10.1145/3369780
Cui LHu HYu SYan QMing ZWen ZLu N(2018)DDSEJournal of Network and Computer Applications10.1016/j.jnca.2017.12.003103:C(119-130)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1016/j.jnca.2017.12.003
Hatua ANguyen TSung AAnjum ASill AZhao XFarid MPallickara SCao J(2017)Information Diffusion on TwitterProceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies10.1145/3148055.3148078(157-167)Online publication date: 5-Dec-2017
https://dl.acm.org/doi/10.1145/3148055.3148078
Huang XYe YXiong LWang SYang X(2016)Clustering time-stamped data using multiple nonnegative matrices factorizationKnowledge-Based Systems10.5555/3034192.3034315114:C(88-98)Online publication date: 15-Dec-2016
https://dl.acm.org/doi/10.5555/3034192.3034315
Bindu PThilagam P(2016)Mining social networks for anomaliesJournal of Network and Computer Applications10.1016/j.jnca.2016.02.02168:C(213-229)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.jnca.2016.02.021

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents