# Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data

arXiv: https://arxiv.org/abs/2401.00893 · HTML: https://mlsanity.com/paper/2401.00893/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 73 references that are in the map.
- First posted on arXiv 31 Dec 2023.

## Builds on (most cited first, 25 of 25)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014)
- [Heterogeneous Graph Neural Networks for Malicious Account Detection](https://mlsanity.com/paper/2002.12307/) (2020)
- [Characterizing and Detecting Hateful Users on Twitter](https://mlsanity.com/paper/1803.08977/) (2018)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embedding](https://mlsanity.com/paper/2006.01321/) (2020)
- [Botometer 101: Social bot practicum for computational social scientists](https://mlsanity.com/paper/2201.01608/) (2022)
- [#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election](https://mlsanity.com/paper/2010.00600/) (2020)
- [Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks](https://mlsanity.com/paper/2207.08349/) (2022)
- [Scalable Graph Neural Network Training: The Case for Sampling](https://mlsanity.com/paper/2105.02315/) (2021)
- [Propaganda and Misinformation on Facebook and Twitter during the Russian Invasion of Ukraine](https://mlsanity.com/paper/2212.00419/) (2022)
- [Identifying and Characterizing Behavioral Classes of Radicalization within the QAnon Conspiracy on Twitter](https://mlsanity.com/paper/2209.09339/) (2022)
- ["It's Not Just Hate'': A Multi-Dimensional Perspective on Detecting Harmful Speech Online](https://mlsanity.com/paper/2210.15870/) (2022)
- [Tweets in Time of Conflict: A Public Dataset Tracking the Twitter Discourse on the War Between Ukraine and Russia](https://mlsanity.com/paper/2203.07488/) (2022)
- [How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events](https://mlsanity.com/paper/2209.07614/) (2022)
- [Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure](https://mlsanity.com/paper/2311.05724/) (2023)
- [Social Approval and Network Homophily as Motivators of Online Toxicity](https://mlsanity.com/paper/2310.07779/) (2023)
- [What are Your Pronouns? Examining Gender Pronoun Usage on Twitter](https://mlsanity.com/paper/2207.10894/) (2022)

## Built on by (most cited first, 11 of 11)

- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2502.10708/) (2025)
- [Survey and Experiments on Mental Disorder Detection via Social Media: From Large Language Models and RAG to Agents](https://mlsanity.com/paper/2504.02800/) (2025)
- [Large Language Models in Politics and Democracy: A Comprehensive Survey](https://mlsanity.com/paper/2412.04498/) (2024)
- [A Survey on Moral Foundation Theory and Pre-Trained Language Models: Current Advances and Challenges](https://mlsanity.com/paper/2409.13521/) (2024)
- [EgoSocial: Benchmarking Proactive Intervention Ability of Omnimodal LLMs via Egocentric Social Interaction Perception](https://mlsanity.com/paper/2510.13105/) (2025)
- [Simulating Filter Bubble on Short-video Recommender System with Large Language Model Agents](https://mlsanity.com/paper/2504.08742/) (2025)
- [Fine-Tuning Large Language Models with QLoRA for Offensive Language Detection in Roman Urdu-English Code-Mixed Text](https://mlsanity.com/paper/2510.03683/) (2025)
- [RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social Networking Services](https://mlsanity.com/paper/2511.07070/) (2025)
- [Limits to Predicting Online Speech Using Large Language Models](https://mlsanity.com/paper/2407.12850/) (2024)
- [Persuasive and Compliant Tendencies Predict Group Decision-Making in Humans and Language Models](https://mlsanity.com/paper/2608.08199/) (2026)

## Abstract (arXiv)

The proliferation of social network data has unlocked unprecedented opportunities for extensive, data-driven exploration of human behavior. The structural intricacies of social networks offer insights into various computational social science issues, particularly concerning social influence and information diffusion. However, modeling large-scale social network data comes with computational challenges. Though large language models make it easier than ever to model textual content, any advanced network representation methods struggle with scalability and efficient deployment to out-of-sample users. In response, we introduce a novel approach tailored for modeling social network data in user detection tasks. This innovative method integrates localized social network interactions with the capabilities of large language models. Operating under the premise of social network homophily, which posits that socially connected users share similarities, our approach is designed to address these challenges. We conduct a thorough evaluation of our method across seven real-world social network datasets, spanning a diverse range of topics and detection tasks, showcasing its applicability to advance research in computational social science.