Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
A measurement-driven analysis of information propagation in the flickr social network
Proceedings of the 18th international conference on World wide web
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Finding influentials based on the temporal order of information adoption in twitter
Proceedings of the 19th international conference on World wide web
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
Proceedings of the 20th international conference on World wide web
Information spreading in context
Proceedings of the 20th international conference on World wide web
Modeling the structure and evolution of discussion cascades
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Realtime analysis of information diffusion in social media
Proceedings of the VLDB Endowment
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User interactions over social networks has been an emergent theme over the last several years. In contrast to previous work we focus on characterizing user communications patterns around an initial post, or conversation root. Specifically, we focus on how other users respond to these roots and how the complete conversation initiated by this root evolves over time. For this purpose we focus our investigation on Twitter, the biggest micro-blogging social network. To the best of our knowledge this is the first such method that is able to reconstruct complete conversations around initial tweets. We propose a robust approach for reconstructing complete conversations and compare the resulting graph structures against those obtained from previous crawling strategies based on keyword searches. Our crawl provides a large scale dataset, ideal for computer scientists to run large scale experimental evaluations, however our dataset is made of a collection of small scale, highly controlled and complete conversation graphs ideal for a sociological investigation. We believe our work will provide the proper dataset to establish concrete collaborations with interdisciplinary expertise.