Digging into Digg: genres of online news
Proceedings of the 2011 iConference
Persuasive language and virality in social networks
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
A model for popularity dynamics to predict hot articles in discussion blog
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
What's trending?: mining topical trends in UGC systems with YouTube as a case study
Proceedings of the Eleventh International Workshop on Multimedia Data Mining
Towards a predictive cache replacement strategy for multimedia content
Journal of Network and Computer Applications
A peek into the future: predicting the evolution of popularity in user generated content
Proceedings of the sixth ACM international conference on Web search and data mining
Discovering content-based behavioral roles in social networks
Decision Support Systems
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Using comment information available from Digg we define a co-participation network between users. We focus on the analysis of this implicit network, and study the behavioral characteristics of users. Using an entropy measure, we infer that users at Digg are not highly focused and participate across a wide range of topics. We also use the comment data and social network derived features to predict the popularity of online content linked at Digg using a classification and regression framework. We show promising results for predicting the popularity scores even after limiting our feature extraction to the first few hours of comment activity that follows a Digg submission.