@inproceedings{99e0159d7565442f86f8088ea89bb15d,
title = "Temporal Pattern of (Re)Tweets Reveal Cascade Migration",
abstract = "Twitter has recently become one of the most popular online social networking websites where users can share news and ideas through messages in the form of tweets. As a tweet gets retweeted from user to user, large cascades of information diffusion are formed over the Twitter follower network. Existing works on cascades have mainly focused on predicting their popularity in terms of size. In this paper, we leverage on the temporal pattern of retweets to model the diffusion dynamics of a cascade. Notably, retweet cascades provide two complementary information: (a) inter-retweet time intervals of retweets, and (b) diffusion of cascade over the underlying follower network. Using datasets from Twitter, we identify two types of cascades based on presence or absence of early peaks in their sequence of inter-retweet intervals. We identify multiple diffusion localities associated with a cascade as it propagates over the network. Our studies reveal the transition of a cascade to a new locality facilitated by pivotal users that are highly cascade dependent following saturation of current locality. We propose an analytical model to show co-occurrence of first peaks and cascade migration to a new locality as well as predict locality saturation from inter-retweet intervals. Finally, we validate these claims from empirical data showing co-occurrence of first peaks and migration with good accuracy; we obtain even better accuracy for successfully classifying saturated and non-saturated diffusion localities from inter-retweet intervals.",
keywords = "Diffusion locality, cascade migration, inter-retweet intervals, pivotal user, Diffusion locality, Cascade migration, Inter-retweet intervals, Pivotal user",
author = "Bhowmick, {Ayan Kumar} and Martin Gueuning and Jean-Charles Delvenne and Renaud Lambiotte and Bivas Mitra",
note = "Funding Information: ACKNOWLEDGEMENTS This work has been partially supported by the DST-BELSPO funded Indo-Belgian collaborative project titled {"}DYCIN - Analyzing the Dynamics of Critical Information Diffusion on Social Media: A Network Science initiative{"}, and by the grant ARC {"}Mining and Optimization of Big Data Models{"} of the Communaute Francaise de Belgique. Funding Information: This work has been partially supported by the DST-BELSPO funded Indo-Belgian collaborative project titled {"}DYCIN - Analyzing the Dynamics of Critical Information Diffusion on Social Media: A Network Science initiative{"}, and by the grant ARC {"}Mining and Optimization of Big Data Models{"} of the Communaute Francaise de Belgique. Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.",
year = "2017",
month = jul,
day = "31",
doi = "10.1145/3110025.3110084",
language = "English",
isbn = "978-1-4503-4993-2",
series = "Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017",
publisher = "ACM Press",
pages = "483--488",
editor = "Jana Diesner and Elena Ferrari and Guandong Xu",
booktitle = "Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2017",
address = "United States",
}