Paper Info
Title
Information spreading in context
Abstract
Information spreading processes are central to human interactions. Despite recent studies in online domains, little is known about factors that could affect the dissemination of a single piece of information. In this paper, we address this challenge by combining two related but distinct datasets, collected from a large scale privacy-preserving distributed social sensor system. We find that the social and organizational context significantly impacts to whom and how fast people forward information. Yet the structures within spreading processes can be well captured by a simple stochastic branching model, indicating surprising independence of context. Our results build the foundation of future predictive models of information flow and provide significant insights towards design of communication platforms.
Year
DOI
Venue
2011
10.1145/1963405.1963508
WWW
Keywords
Field
DocType
structure,fast people,information spread,future predictive model,information flow,social networks,large scale,human interaction,communication platform,social sensor system,network science,context,online domain,organizational context,distinct datasets,social network,prediction model
Data science,Network science,Data mining,Information flow (information theory),World Wide Web,Social network,Organizational context,Computer science,Sensor system,Artificial intelligence,Machine learning,Branching (version control)
Conference
Citations 
PageRank 
References 
36
1.70
20
Authors
6
Name
Order
Citations
PageRank
Dashun Wang162727.09
Zhen Wen273741.46
Hanghang Tong33560202.37
Ching-yung Lin41963175.16
Chaoming Song558023.58
Albert-lászló Barabási646491107.35