Machine Learning
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Meme-tracking and the dynamics of the news cycle
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
Outtweeting the twitterers - predicting information cascades in microblogs
WOSN'10 Proceedings of the 3rd conference on Online social networks
Selecting information diffusion models over social networks for behavioral analysis
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Modeling Information Diffusion in Implicit Networks
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
Patterns of temporal variation in online media
Proceedings of the fourth ACM international conference on Web search and data mining
Information spreading in context
Proceedings of the 20th international conference on World wide web
Visual analysis of implicit social networks for suspicious behavior detection
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Information diffusion in online social networks
Proceedings of the 2013 Sigmod/PODS Ph.D. symposium on PhD symposium
Choosing which message to publish on social networks: a contextual bandit approach
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Detecting changes in content and posting time distributions in social media
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Information diffusion in online social networks: a survey
ACM SIGMOD Record
Learning social network embeddings for predicting information diffusion
Proceedings of the 7th ACM international conference on Web search and data mining
Box office prediction based on microblog
Expert Systems with Applications: An International Journal
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Today, online social networks have become powerful tools for the spread of information. They facilitate the rapid and large-scale propagation of content and the consequences of an information -- whether it is favorable or not to someone, false or true -- can then take considerable proportions. Therefore it is essential to provide means to analyze the phenomenon of information dissemination in such networks. Many recent studies have addressed the modeling of the process of information diffusion, from a topological point of view and in a theoretical perspective, but we still know little about the factors involved in it. With the assumption that the dynamics of the spreading process at the macroscopic level is explained by interactions at microscopic level between pairs of users and the topology of their interconnections, we propose a practical solution which aims to predict the temporal dynamics of diffusion in social networks. Our approach is based on machine learning techniques and the inference of time-dependent diffusion probabilities from a multidimensional analysis of individual behaviors. Experimental results on a real dataset extracted from Twitter show the interest and effectiveness of the proposed approach as well as interesting recommendations for future investigation.