Automated representation learning is behind many recent success stories in
machine learning. It is often used to transfer knowledge learned from a large
dataset (e.g., raw text) to tasks for which only a small number of training
examples are available. In this paper, we review recent advance in learning to
represent social media users in low-dimensional embeddings. The technology is
critical for creating high performance social media-based human traits and
behavior models since the ground truth for assessing latent human traits and
behavior is often expensive to acquire at a large scale. In this survey, we
review typical methods for learning a unified user embeddings from
heterogeneous user data (e.g., combines social media texts with images to learn
a unified user representation). Finally we point out some current issues and
future directions.

该研究回顾了最近在社交媒体用户的表征学习领域的进展，并提供了从异构用户数据（例如，将社交媒体文本与图像相结合来学习统一用户表征）中学习统一用户嵌入的典型方法，该技术对于创建高性能的基于社交媒体的人类特征和行为模型至关重要。