# TBAM: Towards An Agent-Based Model to Enrich Twitter Data

arXiv: https://arxiv.org/abs/2302.00128 · HTML: https://mlsanity.com/paper/2302.00128/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2023.

## Built on by (most cited first, 2 of 2)

- [Towards Modeling Data Quality and Machine Learning Model Performance](https://mlsanity.com/paper/2412.05882/) (2024)
- [Domain-Generalization to Improve Learning in Meta-Learning Algorithms](https://mlsanity.com/paper/2508.09418/) (2025)

## Abstract (arXiv)

Twitter (one example of microblogging) is widely being used by researchers to understand human behavior, specifically how people behave when a significant event occurs and how it changes user microblogging patterns. The changing microblogging behavior can reveal patterns that can help in detecting real-world events. However, the Twitter data that is available has limitations, such as, it is incomplete and noisy and the samples are irregular. In this paper we create a model, called Twitter Behavior Agent-Based Model (TBAM) to simulate Twitter pattern and behavior using Agent-Based Modeling (ABM). The generated data from ABM simulations can be used in place or to complement the real-world data toward improving the accuracy of event detection. We confirm the validity of our model by finding the cross-correlation between the real data collected from Twitter and the data generated using TBAM.