# Predicting Social Media User Actions: A Hybrid Approach for Common and Rare Behavior Prediction on Bluesky

arXiv: https://arxiv.org/abs/2511.17241 · HTML: https://mlsanity.com/paper/2511.17241/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 21 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [LLM Generated Persona is a Promise with a Catch](https://mlsanity.com/paper/2503.16527/) (2025)
- [Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms](https://mlsanity.com/paper/2310.05984/) (2023)
- [Y Social: an LLM-powered Social Media Digital Twin](https://mlsanity.com/paper/2408.00818/) (2024)
- [A Simulation System Towards Solving Societal-Scale Manipulation](https://mlsanity.com/paper/2410.13915/) (2024)
- [SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research](https://mlsanity.com/paper/2310.14757/) (2023)
- [Can LLMs Simulate Social Media Engagement? A Study on Action-Guided Response Generation](https://mlsanity.com/paper/2502.12073/) (2025)
- [Measuring the Effect of Influential Messages on Varying Personas](https://mlsanity.com/paper/2305.16470/) (2023)
- [textttBluePrint: A Social Media User Dataset for LLM Persona Evaluation and Training](https://mlsanity.com/paper/2510.02343/) (2025)
- [Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction](https://mlsanity.com/paper/1911.07156/) (2019)
- [Knowledge Boundary and Persona Dynamic Shape A Better Social Media Agent](https://mlsanity.com/paper/2403.19275/) (2024)

## Abstract (arXiv)

Understanding and predicting user behavior on social media platforms is crucial for content recommendation and platform design. While existing approaches focus primarily on common actions like retweeting and liking, the prediction of rare but significant behaviors remains largely unexplored. This paper presents a hybrid methodology for social media user behavior prediction that addresses both frequent and infrequent actions across a diverse action vocabulary. We evaluate our approach on a large-scale Bluesky dataset containing 6.4 million conversation threads spanning 12 distinct user actions across 25 persona clusters. Our methodology combines four complementary approaches: (i) a lookup database system based on historical response patterns; (ii) persona-specific LightGBM models with engineered temporal and semantic features for common actions; (iii) a specialized hybrid neural architecture fusing textual and temporal representations for rare action classification; and (iv) generation of text replies. Our persona-specific models achieve an average macro F1-score of 0.64 for common action prediction, while our rare action classifier achieves 0.56 macro F1-score across 10 rare actions. These results demonstrate that effective social media behavior prediction requires tailored modeling strategies recognizing fundamental differences between action types. Our approach achieved first place in the SocialSim: Social-Media Based Personas challenge organized at the Social Simulation with LLMs workshop at the Conference on Language Modeling (COLM 2025).