  We consider the task of linking social media accounts that belong to the same
author in an automated fashion on the basis of the content and metadata of
their corresponding document streams. We focus on learning an embedding that
maps variable-sized samples of user activity -- ranging from single posts to
entire months of activity -- to a vector space, where samples by the same
author map to nearby points. The approach does not require human-annotated data
for training purposes, which allows us to leverage large amounts of social
media content. The proposed model outperforms several competitive baselines
under a novel evaluation framework modeled after established recognition
benchmarks in other domains. Our method achieves high linking accuracy, even
with small samples from accounts not seen at training time, a prerequisite for
practical applications of the proposed linking framework.
