Exploring Image Virality in Google Plus

Reactions to posts in an online social network show different dynamics depending on several textual features of the corresponding content. Do similar dynamics exist when images are posted? Exploiting a novel dataset of posts, gathered from the most popular Google+ users, we try to give an answer to such a question. We describe several virality phenomena that emerge when taking into account visual characteristics of images (such as orientation, mean saturation, etc.). We also provide hypotheses and potential explanations for the dynamics behind them, and include cases for which common-sense expectations do not hold true in our experiments.Comment: 8 pages, 8 figures. IEEE/ASE SocialCom 201

Steps toward a classifier for the Virtual Observatory. I. Classifying the SDSS photometric archive

Modern photometric multiband digital surveys produce large amounts of data that, in order to be effectively exploited, need automatic tools capable to extract from photometric data an objective classification. We present here a new method for classifying objects in large multi-parametric photometric data bases, consisting of a combination of a clustering algorithm and a cluster agglomeration tool. The generalization capabilities and the potentialities of this approach are tested against the complexity of the Sloan Digital Sky Survey archive, for which an example of application is reported.Comment: To appear in the Proceedings of the "1st Workshop of Astronomy and Astrophysics for Students" - Naples, 19-20 April 200