  Information in networks is non-uniformly distributed, enabling individuals in
certain network positions to get preferential access to information. Social
scientists have developed influential theories about the role of network
structure in information access. These theories were validated through numerous
studies, which examined how individuals leverage their social networks for
competitive advantage, such as a new job or higher compensation. It is not
clear how these theories generalize to online networks, which differ from
real-world social networks in important respects, including asymmetry of social
links. We address this problem by analyzing how users of the social news
aggregator Digg adopt stories recommended by friends, i.e., users they follow.
We measure the impact different factors, such as network position and activity
rate; have on access to novel information, which in Digg's case means set of
distinct news stories. We show that a user can improve his information access
by linking to active users, though this becomes less effective as the number of
friends, or their activity, grows due to structural network constraints. These
constraints arise because users in structurally diverse position within the
follower graph have topically diverse interests from their friends. Moreover,
though in most cases user's friends are exposed to almost all the information
available in the network, after they make their recommendations, the user sees
only a small fraction of the available information. Our study suggests that
cognitive and structural bottlenecks limit access to novel information in
online social networks.
