  Social world knowledge is a key ingredient in effective communication and
information processing by humans and machines alike. As of today, there exist
many knowledge bases that represent factual world knowledge. Yet, there is no
resource that is designed to capture social aspects of world knowledge. We
believe that this work makes an important step towards the formulation and
construction of such a resource. We introduce SocialVec, a general framework
for eliciting low-dimensional entity embeddings from the social contexts in
which they occur in social networks. In this framework, entities correspond to
highly popular accounts which invoke general interest. We assume that entities
that individual users tend to co-follow are socially related, and use this
definition of social context to learn the entity embeddings. Similar to word
embeddings which facilitate tasks that involve text semantics, we expect the
learned social entity embeddings to benefit multiple tasks of social flavor. In
this work, we elicited the social embeddings of roughly 200K entities from a
sample of 1.3M Twitter users and the accounts that they follow. We employ and
gauge the resulting embeddings on two tasks of social importance. First, we
assess the political bias of news sources in terms of entity similarity in the
social embedding space. Second, we predict the personal traits of individual
Twitter users based on the social embeddings of entities that they follow. In
both cases, we show advantageous or competitive performance using our approach
compared with task-specific baselines. We further show that existing entity
embedding schemes, which are fact-based, fail to capture social aspects of
knowledge. We make the learned social entity embeddings available to the
research community to support further exploration of social world knowledge and
its applications.
