Article

Outtweeting the twitterers - predicting information cascades in microblogs

Authors:

Wojciech Galuba,

Dipanjan Chakraborty,

Zoran Despotovic,

Wolfgang KellererAuthors Info & Claims

WOSN'10: Proceedings of the 3rd Wonference on Online social networks

Page 3

Published: 22 June 2010 Publication History

Abstract

Microblogging sites are a unique and dynamic Web 2.0 communication medium. Understanding the information flow in these systems can not only provide better insights into the underlying sociology, but is also crucial for applications such as content ranking, recommendation and filtering, spam detection and viral marketing. In this paper, we characterize the propagation of URLs in the social network of Twitter, a popular microblogging site. We track 15 million URLs exchanged among 2.7 million users over a 300 hour period. Data analysis uncovers several statistical regularities in the user activity, the social graph, the structure of the URL cascades and the communication dynamics. Based on these results we propose a propagation model that predicts which users are likely to mention which URLs. The model correctly accounts for more than half of the URL mentions in our data set, while maintaining a false positive rate lower than 15%.

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WOSN'10: Proceedings of the 3rd Wonference on Online social networks

June 2010

13 pages

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USENIX Association

United States

Publication History

Published: 22 June 2010

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Cited By

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https://dl.acm.org/doi/10.1145/3494563
Wang RHuang ZLiu SShao HLiu DLi JWang TSun DYao SAbdelzaher TDiaz FShah CSuel TCastells PJones RSakai T(2021)DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion PredictionProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3462934(163-172)Online publication date: 11-Jul-2021
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https://dl.acm.org/doi/10.1145/3308558.3313560
Gayberi MOguducu SChbeir RManolopoulos YIlarri SPapadopoulos A(2019)Popularity Prediction of Posts in Social Networks Based on User, Post and Image FeaturesProceedings of the 11th International Conference on Management of Digital EcoSystems10.1145/3297662.3365812(9-15)Online publication date: 12-Nov-2019
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