The nature of statistical learning theory
The nature of statistical learning theory
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Social Information Processing in News Aggregation
IEEE Internet Computing
User Participation in Social Media: Digg Study
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Analysis of social voting patterns on digg
Proceedings of the first workshop on Online social networks
The lie detector: explorations in the automatic recognition of deceptive language
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Digging Digg: Comment Mining, Popularity Prediction, and Social Network Analysis
WISM '09 Proceedings of the 2009 International Conference on Web Information Systems and Mining
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
Predicting the popularity of online content
Communications of the ACM
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This paper aims to provide new insights on the concept of virality and on its structure - especially in social networks. We argue that: (a) virality is a phenomenon strictly connected to the nature of the content being spread (b) virality is a phenomenon with many affective responses, i.e. under this generic term several different effects of persuasive communication are comprised. To give ground to our claims, we provide initial experiments in a machine learning framework to show how various aspects of virality can be predicted according to content features. We further provide a class-based psycholinguistic analysis of the features salient for virality components.