text: 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.
  @cite_26: Why are monkeys, apes and humans such social animals and how did we get that way? As [Dunbar][1] describes in his Perspective, such questions have puzzled primatologists for decades. Two new studies in baboons provide some answers by demonstrating that the tighter the social bonds of female baboons the greater is their reproductive fitness ([ .][2]), and that female baboons are able to simultaneously assess the rank and kinship of other baboon females ([ ][3].). @PARASPLIT [1]: http: www.sciencemag.org cgi content full 302 5648 1160 [2]: http: www.sciencemag.org cgi content short 302 5648 1231 [3]: http: www.sciencemag.org cgi content short 302 5648 1234
  @cite_21: 
  @cite_8: Microblogging and mobile devices appear to augment human social capabilities, which raises the question whether they remove cognitive or biological constraints on human communication. In this paper we analyze a dataset of Twitter conversations collected across six months involving 1.7 million individuals and test the theoretical cognitive limit on the number of stable social relationships known as Dunbar's number. We find that the data are in agreement with Dunbar's result; users can entertain a maximum of 100–200 stable relationships. Thus, the ‘economy of attention’ is limited in the online world by cognitive and biological constraints as predicted by Dunbar's theory. We propose a simple model for users' behavior that includes finite priority queuing and time resources that reproduces the observed social behavior.
summary: Recently, researchers recognized that cognitive constraints are important to defining social interactions online and in the real world. The number of social relationships that people can maintain is limited to about 150 @cite_26 . This is similar to the limit of the number of conversation partners that Twitter users have @cite_8 . Cognitive constraints, specifically, divided attention, was also shown to limit the spread of information on Twitter @cite_21 . We find a dependence of user activity on network structure that mirrors those imposed by cognitive constraints. We observe that user's activity rate initially increases with the number of friends, until reaching a maximum around 200 friends and then decreases somewhat. The cognitive limits constraints the potential exposures to new information as users have too much information recommended by many friends. Further we argued that the limited activities in high diverse network is either because their lack of interest in processing recommended information, or because they are less willing to devote the greater cognitive resources required to process this diverse information.