I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Structure and Network in the YouTube Core
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
Characterizing social cascades in flickr
Proceedings of the first workshop on Online social networks
Growth of the flickr social network
Proceedings of the first workshop on Online social networks
Learning spectral graph transformations for link prediction
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Analysis of community-contributed space-and time-referenced data (example of Panoramio photos)
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Can social features help learning to rank youtube videos?
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
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In this paper we analyze and compare three popular content creation and sharing websites, namely Panoramio, YouTube and Epinions. This analysis aims in advancing our understanding of Web Social Media and their impact, and may be useful in creating feedback mechanisms for increasing user participation and sharing. For each of the three websites, we select five fundamental factors appearing in all content centered Web Social Media and we use regression analysis to calculate their correlation. We present findings of statistically important correlations among these key factors and we rank the discovered correlations according to the degree of their influence. Furthermore, we perform analysis of variance in distinct subgroups of the collected data and we discuss differences found in the characteristics of these subgroups and how these differences may affect correlation results. Although we acknowledge that correlation does not imply causality, the discovered correlations may be a first step towards discovering causality laws behind content contribution, commenting and the formulation of friendship relations. These causality laws are useful for boosting the user participation in social media.