Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/107833
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Type: Conference paper
Title: Twitter knows: understanding the emergence of topics in social networks
Author: Birdsey, L.
Szabo, C.
Teo, Y.
Citation: Proceedings of the 2015 Winter Simulation Conference, 2016 / Yilmaz, L., Chan, W., Moon, I., Roeder, T., Macal, C., Rosetti, M. (ed./s), vol.2016-February, pp.4009-4020
Publisher: IEEE
Issue Date: 2016
Series/Report no.: Winter Simulation Conference Proceedings
ISBN: 9781467397438
ISSN: 0891-7736
1558-4305
Conference Name: Winter Simulation Conference (WSC) (06 Dec 2015 - 09 Dec 2015 : Huntington Beach, CA)
Statement of
Responsibility: 
Lachlan Birdsey, Claudia Szabo, Yong Meng Teo
Abstract: Social networks such as Twitter and Facebook are important and widely used communication environments that exhibit scale, complexity, node interaction, and emergent behavior. In this paper, we analyze emergent behavior in Twitter and propose a definition of emergent behavior focused on the pervasiveness of a topic within a community. We extend an existing stochastic model for user behavior, focusing on advocatefollower relationships. The new user posting model includes retweets, replies, and mentions as user responses. To capture emergence, we propose a RPBS (Rising, Plateau, Burst and Stabilization) topic pervasiveness model with a new metric that captures how frequent and in what form the community is talking about a particular topic. Our initial validation compares our model with four Twitter datasets. Our extensive experimental analysis allows us to explore several “what-if” scenarios with respect to topic and knowledge sharing, showing how a pervasive topic evolves given various popularity scenarios.
Rights: ©2015 IEEE
RMID: 0030049823
DOI: 10.1109/WSC.2015.7408555
Appears in Collections:Computer Science publications

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