# You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP

arXiv: https://arxiv.org/abs/1909.00412 · HTML: https://mlsanity.com/paper/1909.00412/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2019.

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

- [Abuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detection](https://mlsanity.com/paper/2103.14916/) (2021)
- [Large Human Language Models: A Need and the Challenges](https://mlsanity.com/paper/2312.07751/) (2023)
- [Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future](https://mlsanity.com/paper/2403.14659/) (2024)
- [CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection](https://mlsanity.com/paper/2204.06389/) (2022)
- [Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability](https://mlsanity.com/paper/2103.17191/) (2021)
- [Analysis of Twitter Users' Lifestyle Choices using Joint Embedding Model](https://mlsanity.com/paper/2104.03189/) (2021)
- [Twitter User Representation Using Weakly Supervised Graph Embedding](https://mlsanity.com/paper/2108.08988/) (2021)
- [Identifying the Adoption or Rejection of Misinformation Targeting COVID-19 Vaccines in Twitter Discourse](https://mlsanity.com/paper/2202.09445/) (2022)
- [PerPLM: Personalized Fine-tuning of Pretrained Language Models via Writer-specific Intermediate Learning and Prompts](https://mlsanity.com/paper/2309.07727/) (2023)
- [Language Independent Stance Detection: Social Interaction-based Embeddings and Large Language Models](https://mlsanity.com/paper/2210.05715/) (2022)
- [How Well Do You Know Your Audience? Toward Socially-aware Question Generation](https://mlsanity.com/paper/2110.08445/) (2021)
- [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)
- [TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference](https://mlsanity.com/paper/2601.20032/) (2026)

## Abstract (arXiv)

Information about individuals can help to better understand what they say, particularly in social media where texts are short. Current approaches to modelling social media users pay attention to their social connections, but exploit this information in a static way, treating all connections uniformly. This ignores the fact, well known in sociolinguistics, that an individual may be part of several communities which are not equally relevant in all communicative situations. We present a model based on Graph Attention Networks that captures this observation. It dynamically explores the social graph of a user, computes a user representation given the most relevant connections for a target task, and combines it with linguistic information to make a prediction. We apply our model to three different tasks, evaluate it against alternative models, and analyse the results extensively, showing that it significantly outperforms other current methods.