  This paper introduces SocialVec, a general framework for eliciting social
world knowledge from social networks, and applies this framework to Twitter.
SocialVec learns low-dimensional embeddings of popular accounts, which
represent entities of general interest, based on their co-occurrences patterns
within the accounts followed by individual users, thus modeling entity
similarity in socio-demographic terms. Similar to word embeddings, which
facilitate tasks that involve text processing, we expect social entity
embeddings to benefit tasks of social flavor. We have learned social embeddings
for roughly 200,000 popular accounts from a sample of the Twitter network that
includes more than 1.3 million users and the accounts that they follow, and
evaluate the resulting embeddings on two different tasks. The first task
involves the automatic inference of personal traits of users from their social
media profiles. In another study, we exploit SocialVec embeddings for gauging
the political bias of news sources in Twitter. In both cases, we prove
SocialVec embeddings to be advantageous compared with existing entity embedding
schemes. We will make the SocialVec entity embeddings publicly available to
support further exploration of social world knowledge as reflected in Twitter.
