Learning User Embeddings from Temporal Social Media Data: A Survey
  User-generated data on social media contain rich information about who we
are, what we like and how we make decisions. In this paper, we survey
representative work on learning a concise latent user representation (a.k.a.
user embedding) that can capture the main characteristics of a social media
user. The learned user embeddings can later be used to support different
downstream user analysis tasks such as personality modeling, suicidal risk
assessment and purchase decision prediction. The temporal nature of
user-generated data on social media has largely been overlooked in much of the
existing user embedding literature. In this survey, we focus on research that
bridges the gap by incorporating temporal/sequential information in user
representation learning. We categorize relevant papers along several key
dimensions, identify limitations in the current work and suggest future
research directions.
