ACM SIGKDD Explorations Newsletter
Communications of the ACM - The Blogosphere
Novelty detection: the TREC experience
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
BlogCentral: the role of internal blogs at work
CHI '07 Extended Abstracts on Human Factors in Computing Systems
Unsupervised prediction of citation influences
Proceedings of the 24th international conference on Machine learning
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Proceedings of the 18th international conference on World wide web
Content Quality Assessment Related Frameworks for Social Media
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Social Knowledge-Driven Music Hit Prediction
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Ranking Comments on the Social Web
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Predicting the volume of comments on online news stories
Proceedings of the 18th ACM conference on Information and knowledge management
Estimating number of citations using author reputation
SPIRE'07 Proceedings of the 14th international conference on String processing and information retrieval
IEEE Transactions on Information Theory
On the Relationship between Novelty and Popularity of User-Generated Content
ACM Transactions on Intelligent Systems and Technology (TIST)
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This work deals with the task of predicting the popularity of user-generated content. We demonstrate how the novelty of newly published content plays an important role in affecting its popularity. We study three dimensions of novelty: contemporaneous novelty, self novelty, and discussion novelty. We demonstrate the contribution of the new novelty measures to estimating blog-post popularity by predicting the number of comments expected for a fresh post. We further demonstrate how novelty based measures can be utilized for predicting the citation volume of academic papers.