Predicting trending messages and diffusion participants in microblogging network

Bian, Jingwen, Yang, Yang and Chua, Tat-Seng (2014). Predicting trending messages and diffusion participants in microblogging network. In: SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014, Gold Coast, QLD Australia, (537-546). 6-11 July 2014. doi:10.1145/2600428.2609616

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Author Bian, Jingwen
Yang, Yang
Chua, Tat-Seng
Title of paper Predicting trending messages and diffusion participants in microblogging network
Conference name 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014
Conference location Gold Coast, QLD Australia
Conference dates 6-11 July 2014
Convener Shlomo Geva
Proceedings title SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of Publication New York, NY United States
Publisher Association for Computing Machinery
Publication Year 2014
Sub-type Fully published paper
DOI 10.1145/2600428.2609616
ISBN 9781450322591
145032259X
9781450322577
Start page 537
End page 546
Total pages 10
Chapter number 57
Total chapters 229
Collection year 2015
Language eng
Formatted Abstract/Summary
Microblogging services have emerged as an essential way to strengthen the communications among individuals. One of the most important features of microblog over traditional social networks is the extensive proliferation in information diffusion. As the outbreak of information diffusion often brings in valuable opportunities or devastating effects, it will be beneficial if a mechanism can be provided to predict whether a piece of information will become viral, and which part of the network will participate in propagating this information. In this work, we define three types of influences, namely, interest-oriented influence, social-oriented influence, and epidemic-oriented influence, that will affect a user's decision on whether to perform a diffusion action. We propose a diffusion-targeted influence model to differentiate and quantify various types of influence. Further we model the problem of diffusion prediction by factorizing a user's intention to transmit a microblog into these influences. The learned prediction model is then used to predict the future diffusion state of any new microblog. We conduct experiments on a real-world microblogging dataset to evaluate our method, and the results demonstrate the superiority of the proposed framework as compared to the state-of-the-art approaches.
Keyword Diffusion prediction
Social influence analysis
Social network
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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