Research Note: Multinational Diffusion Models: An Alternative Framework
Marketing Science
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Tracking Information Epidemics in Blogspace
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Comments on "A New Product Growth for Model Consumer Durables"
Management Science
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamical Processes on Complex Networks
Dynamical Processes on Complex Networks
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Power-Law Distributions in Empirical Data
SIAM Review
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Networks: An Introduction
Proceedings of the 20th international conference on World wide web
Inferring Networks of Diffusion and Influence
ACM Transactions on Knowledge Discovery from Data (TKDD)
Information diffusion and external influence in networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Spatial influence vs. community influence: modeling the global spread of social media
Proceedings of the 21st ACM international conference on Information and knowledge management
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Increasingly, diverse online social networks are locally and globally interconnected by sharing information in the Web ecosystem. Accordingly, emergent macro-level phenomena have been observed, such as global spread of news across different types of social media. Such real-world diffusion is hard to define with a single social platform alone since dynamic influences between heterogeneous social networks are not negligible. Also, the underlying structural property of networks is important, as it drives the diffusion process in a stochastic way. In this paper, we propose a macro-level diffusion model with a probabilistic approach by combining both heterogeneity and structural connectivity of social networks. As real-world phenomena, we take cases from news diffusion across News, social networking sites (SNS), and Blog media using the ICWSM'11 Spinn3r dataset which contains over 386 million Web documents covering a one-month period in early 2011. We find that influence between different media types is varied by context of information. News media are the most influential in the Arts and Economy categories, while SNS and Blog media are in the Politics and Culture categories, respectively. Also, controversial topics such as political protests and multiculturalism failure tend to spread concurrently across social media, while entertainment topics such as film releases and celebrities are likely driven by internal interactions within single social platforms. We expect that the proposed model applies to a wider class of diffusion phenomena in diverse fields including the social sciences, marketing, and neuroscience, and that it provides a way of interpreting dynamics of meta-populations in terms of strength and directionality of influences among them.