# Social Media-based User Embedding: A Literature Review

arXiv: https://arxiv.org/abs/1907.00725 · HTML: https://mlsanity.com/paper/1907.00725/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2019.

## Built on by (most cited first, 14 of 14)

- [Counter-Empirical Attacking based on Adversarial Reinforcement Learning for Time-Relevant Scoring System](https://mlsanity.com/paper/2311.05144/) (2023)
- [Modality-free Graph In-context Alignment](https://mlsanity.com/paper/2603.13434/) (2026)
- [Bridging Social Media and Search Engines: Dredge Words and the Detection of Unreliable Domains](https://mlsanity.com/paper/2406.11423/) (2024)
- [User Factor Adaptation for User Embedding via Multitask Learning](https://mlsanity.com/paper/2102.11103/) (2021)
- [Learning User Embeddings from Temporal Social Media Data: A Survey](https://mlsanity.com/paper/2105.07996/) (2021)
- [Enriching Unsupervised User Embedding via Medical Concepts](https://mlsanity.com/paper/2203.10627/) (2022)
- [From Text to Context: An Entailment Approach for News Stakeholder Classification](https://mlsanity.com/paper/2405.08751/) (2024)
- [Predicting User Stances from Target-Agnostic Information using Large Language Models](https://mlsanity.com/paper/2409.14395/) (2024)
- [How Well Do You Know Your Audience? Toward Socially-aware Question Generation](https://mlsanity.com/paper/2110.08445/) (2021)
- [Error in the Euclidean Preference Model](https://mlsanity.com/paper/2208.08160/) (2022)
- [Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance](https://mlsanity.com/paper/2401.09724/) (2024)
- [SoMeR: Multi-View User Representation Learning for Social Media](https://mlsanity.com/paper/2405.05275/) (2024)
- [Do We Trust What They Say or What They Do? A Multimodal User Embedding Provides Personalized Explanations](https://mlsanity.com/paper/2409.02965/) (2024)
- [POLAR:A Per-User Association Test in Embedding Space](https://mlsanity.com/paper/2603.15950/) (2026)

## Abstract (arXiv)

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.