The dynamics of attention in social media tend to obey power laws. Attention concentrates on a relatively small number of popular items neglecting the vast majority of content produced by the crowd. Although popularity can be an indication of the perceived value of an item within its community, previous research has highlighted the gap between success and intrinsic quality. As a result, high quality content that receives low attention remains invisible and relegated to the long tail of the popularity distribution. Moreover, the production and consumption of content is influenced by the underlying social network connecting users by means of friendship or follower-followee relations. This talk will present a large-scale study on the complex intertwinement between quality, popularity and social ties in an online photo sharing platform, proposing a methodology to democratize exposure and foster long term users engagement.
Rossano Schifanella is an Assistant Professor in Computer Science at the University of Turin where he is a member of the Applied Research on Computational Complex Systems group and a Researcher at ISI Foundation within the data science for social impact initiative. He has been a visiting scientist at Nokia Bell Labs, Yahoo Labs and Indiana University where he was applying computational methods to model the behavior of (groups of) individuals and their interactions on social media platforms. His research embraces the creative energy of a range of disciplines across machine learning, network science, urban informatics, computational social science, and data visualization. For more details, visit http://www.di.unito.it/~schifane.
Rossano Schifanella is an Assistant Professor in Computer Science at the University of Turin where he is a member of the Applied Research on Computational Complex Systems group and a Researcher at ISI Foundation within the data science for social impact initiative. He has been a visiting scientist at Nokia Bell Labs, Yahoo Labs and Indiana University where he was applying computational methods to model the behavior of (groups of) individuals and their interactions on social media platforms. His research embraces the creative energy of a range of disciplines across machine learning, network science, urban informatics, computational social science, and data visualization. For more details, visit http://www.di.unito.it/~schifane.
