@inproceedings{5d74afec8c344ec19f09253ad09242e7,
title = "Implementation of chaotic analysis on retweet time series",
abstract = "Retweet has become one of the most prominent feature on social networks and an important mean for secondary content promotion. Most existing investigations of retweet behaviors on social networks are conducted based on empirical studies or information diffusion models (such as stochastic process or cascading model). To the best of our knowledge, such a retweet process has not been investigated as a chaotic process. In this paper, we have first examined that retweet time series by 0-1 test where the results provide identification of chaotic behaviors. Furthermore, taking into account of the proven chaotic characteristic, chaos LS-SVM prediction method is applied to form predictions using only a small fraction of the retweet time series. Our evaluation on Sina Weibo dataset and comparisons with a Bayesian model and strawman modal show that this nonlinear prediction method can translate to good step ahead forecasts and perform high accuracy in retweet prediction.",
keywords = "Chaotic analysis, LS-SVM, Nonlinear prediction, Retweet, Social network",
author = "Yuanyuan Bao and Chengqi Yi and Jingchi Jiang and Yibo Xue and Yingfei Dong",
note = "Publisher Copyright: {\textcopyright} 2015 ACM.; 7th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 ; Conference date: 25-08-2015 Through 28-08-2015",
year = "2015",
month = aug,
day = "25",
doi = "10.1145/2808797.2808881",
language = "英语",
series = "Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015",
publisher = "Association for Computing Machinery ",
pages = "1225--1231",
editor = "Jian Pei and Fabrizio Silvestri and Jie Tang",
booktitle = "Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015",
address = "美国",
}