  Individuals' access to information in a social network depends on its
distributed and where in the network individuals position themselves. However,
individuals have limited capacity to manage their social connections and
process information. In this work, we study how this limited capacity and
network structure interact to affect the diversity of information social media
users receive. Previous studies of the role of networks in information access
were limited in their ability to measure the diversity of information. We
address this problem by learning the topics of interest to social media users
by observing messages they share online with their followers. We present a
probabilistic model that incorporates human cognitive constraints in a
generative model of information sharing. We then use the topics learned by the
model to measure the diversity of information users receive from their social
media contacts. We confirm that users in structurally diverse network
positions, which bridge otherwise disconnected regions of the follower graph,
are exposed to more diverse information. In addition, we identify user effort
as an important variable that mediates access to diverse information in social
media. Users who invest more effort into their activity on the site not only
place themselves in more structurally diverse positions within the network than
the less engaged users, but they also receive more diverse information when
located in similar network positions. These findings indicate that the
relationship between network structure and access to information in networks is
more nuanced than previously thought.
