  Users of social networks often focus on specific areas of that network,
leading to the well-known "filter bubble" effect. Connecting people to a new
area of the network in a way that will cause them to become active in that area
could help alleviate this effect and improve social welfare.
  Here we present preliminary analysis of network referrals, that is, attempts
by users to connect peers to other areas of the network. We classify these
referrals by their efficiency, i.e., the likelihood that a referral will result
in a user becoming active in the new area of the network. We show that by using
features describing past experience of the referring author and the content of
their messages we are able to predict whether referral will be effective,
reaching an AUC of 0.87 for those users most experienced in writing efficient
referrals. Our results represent a first step towards algorithmically
constructing efficient referrals with the goal of mitigating the "filter
bubble" effect pervasive in on line social networks.
