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Social networks have emerged as a critical factor in informationdissemination, search, marketing, expertise and influence discovery, andpotentially an important tool for mobilizing people. Social media has madesocial networks ubiquitous, and also given researchers access to massivequantities of data for empirical analysis. These data sets offer a rich sourceof evidence for studying dynamics of individual and group behavior, thestructure of networks and global patterns of the flow of information on them.However, in most previous studies, the structure of the underlying networks wasnot directly visible but had to be inferred from the flow of information fromone individual to another. As a result, we do not yet understand dynamics ofinformation spread on networks or how the structure of the network affects it.We address this gap by analyzing data from two popular social news sites.Specifically, we extract follower graphs of active Digg and Twitter users andtrack how interest in news stories cascades through the graph. We compare andcontrast properties of information cascades on both sites and elucidate whatthey tell us about dynamics of information flow on networks.

Social Contagion: An Empirical Study of Information Spread on Digg and Twitter Follower Graphs