# Learning User Embeddings from Temporal Social Media Data: A Survey

arXiv: https://arxiv.org/abs/2105.07996 · HTML: https://mlsanity.com/paper/2105.07996/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 64 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [Attributed Network Embedding for Learning in a Dynamic Environment](https://mlsanity.com/paper/1706.01860/) (2017)
- [Dynamic stochastic blockmodels for time-evolving social networks](https://mlsanity.com/paper/1403.0921/) (2014)
- [Social Media-based User Embedding: A Literature Review](https://mlsanity.com/paper/1907.00725/) (2019)
- [Quantifying Mental Health from Social Media with Neural User Embeddings](https://mlsanity.com/paper/1705.00335/) (2017)
- [Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks](https://mlsanity.com/paper/1712.04443/) (2017)
- [TIES: Temporal Interaction Embeddings For Enhancing Social Media Integrity At Facebook](https://mlsanity.com/paper/2002.07917/) (2020)
- [Stochastic Block Transition Models for Dynamic Networks](https://mlsanity.com/paper/1411.5404/) (2014)
- [Continuous-Time User Modeling in the Presence of Badges: A Probabilistic Approach](https://mlsanity.com/paper/1702.01948/) (2017)

## Built on by (most cited first, 1 of 1)

- [Incremental user embedding modeling for personalized text classification](https://mlsanity.com/paper/2202.06369/) (2022)

## Abstract (arXiv)

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.