Learning User Embeddings from Temporal Social Media Data: A Survey
This is an incremental survey paper that addresses the problem of improving user analysis tasks like personality modeling and risk assessment for researchers and practitioners in social media analytics.
The paper surveys research on learning user embeddings from temporal social media data to capture user characteristics, highlighting that existing literature often overlooks temporal aspects and aims to bridge this gap by incorporating sequential information.
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.