Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence and the diffusion of user-created content
Proceedings of the 10th ACM conference on Electronic commerce
Predicting the popularity of online content
Communications of the ACM
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Predicting popular messages in Twitter
Proceedings of the 20th international conference companion on World wide web
What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities
Proceedings of the fifth ACM international conference on Web search and data mining
The structure of online diffusion networks
Proceedings of the 13th ACM Conference on Electronic Commerce
Information diffusion and external influence in networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Factors influencing the co-evolution of social and content networks in online social media
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
Structures of broken ties: exploring unfollow behavior on twitter
Proceedings of the 2013 conference on Computer supported cooperative work
A longitudinal study of follow predictors on twitter
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Clash of the Contagions: Cooperation and Competition in Information Diffusion
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
The role of information diffusion in the evolution of social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 23rd international conference on World wide web
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In online social media systems users are not only posting, consuming, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the connection between the two. How does user information posting and seeking behavior interact with the evolution of the underlying social network structure? Here, we study ways in which network structure reacts to users posting and sharing content. We examine the complete dynamics of the Twitter information network, where users post and reshare information while they also create and destroy connections. We find that the dynamics of network structure can be characterized by steady rates of change, interrupted by sudden bursts. Information diffusion in the form of cascades of post re-sharing often creates such sudden bursts of new connections, which significantly change users' local network structure. We also explore the effect of the information content on the dynamics of the network and find evidence that the appearance of new topics and real-world events can lead to significant changes in edge creations and deletions. Lastly, we develop a model that quantifies the dynamics of the network and the occurrence of these bursts as a function of the information spreading through the network. The model can successfully predict which information diffusion events will lead to bursts in network dynamics.