# Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models

arXiv: https://arxiv.org/abs/2202.08964 · HTML: https://mlsanity.com/paper/2202.08964/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 18 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning](https://mlsanity.com/paper/1809.02657/) (2018)
- [Node Embedding over Temporal Graphs](https://mlsanity.com/paper/1903.08889/) (2019)
- [Catching the head, tail, and everything in between: a streaming algorithm for the degree distribution](https://mlsanity.com/paper/1506.02574/) (2015)
- [Massive Multi-Agent Data-Driven Simulations of the GitHub Ecosystem](https://mlsanity.com/paper/1908.05437/) (2019)
- [Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks](https://mlsanity.com/paper/2003.11611/) (2020)

## Abstract (arXiv)

The Volume-Audience-Match simulator, or VAM was applied to predict future activity on Twitter related to international economic affairs. VAM was applied to do timeseries forecasting to predict the: (1) number of total activities, (2) number of active old users, and (3) number of newly active users over the span of 24 hours from the start time of prediction. VAM then used these volume predictions to perform user link predictions. A user-user edge was assigned to each of the activities in the 24 future timesteps. VAM considerably outperformed a set of baseline models in both the time series and user-assignment tasks