# StyloAI: Distinguishing AI-Generated Content with Stylometric Analysis

arXiv: https://arxiv.org/abs/2405.10129 · HTML: https://mlsanity.com/paper/2405.10129/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 16 May 2024.

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

- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [TweepFake: about Detecting Deepfake Tweets](https://mlsanity.com/paper/2008.00036/) (2020)
- [Stylometric Detection of AI-Generated Text in Twitter Timelines](https://mlsanity.com/paper/2303.03697/) (2023)
- [Will ChatGPT get you caught? Rethinking of Plagiarism Detection](https://mlsanity.com/paper/2302.04335/) (2023)
- [Overview of AuTexTification at IberLEF 2023: Detection and Attribution of Machine-Generated Text in Multiple Domains](https://mlsanity.com/paper/2309.11285/) (2023)
- [Classification of Human- and AI-Generated Texts: Investigating Features for ChatGPT](https://mlsanity.com/paper/2308.05341/) (2023)
- [An Empirical Study of AI Generated Text Detection Tools](https://mlsanity.com/paper/2310.01423/) (2023)

## Built on by (most cited first, 17 of 17)

- [A Lightweight Approach to Detection of AI-Generated Texts Using Stylometric Features](https://mlsanity.com/paper/2511.21744/) (2025)
- [Distinguishing AI-Generated and Human-Written Text Through Psycholinguistic Analysis](https://mlsanity.com/paper/2505.01800/) (2025)
- [AI-generated Text Detection: A Multifaceted Approach to Binary and Multiclass Classification](https://mlsanity.com/paper/2505.11550/) (2025)
- [Why AI-Generated Text Detection Fails: Evidence from Explainable AI Beyond Benchmark Accuracy](https://mlsanity.com/paper/2603.23146/) (2026)
- [SKDU at De-Factify 4.0: Natural Language Features for AI-Generated Text-Detection](https://mlsanity.com/paper/2503.22338/) (2025)
- [Stylometry Analysis of Human and Machine Text for Academic Integrity](https://mlsanity.com/paper/2601.01225/) (2026)
- [Assessing Deanonymization Risks with Stylometry-Assisted LLM Agent](https://mlsanity.com/paper/2602.23079/) (2026)
- [IntegrityAI at GenAI Detection Task 2: Detecting Machine-Generated Academic Essays in English and Arabic Using ELECTRA and Stylometry](https://mlsanity.com/paper/2501.05476/) (2025)
- [StylOch at PAN: Gradient-Boosted Trees with Frequency-Based Stylometric Features](https://mlsanity.com/paper/2507.12064/) (2025)
- [AINL-Eval 2025 Shared Task: Detection of AI-Generated Scientific Abstracts in Russian](https://mlsanity.com/paper/2508.09622/) (2025)
- [RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Lifestyle Social Media](https://mlsanity.com/paper/2509.22055/) (2025)
- [Explaining Generalization of AI-Generated Text Detectors Through Linguistic Analysis](https://mlsanity.com/paper/2601.07974/) (2026)
- [Beyond Pattern Matching: Seven Cross-Domain Techniques for Prompt Injection Detection](https://mlsanity.com/paper/2604.18248/) (2026)
- [A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models](https://mlsanity.com/paper/2606.04177/) (2026)
- [Relational Over-Regularization: Graph-Based AI-Generated Text Detection via Sentence Transition Deviation](https://mlsanity.com/paper/2608.26694/) (2026)
- [AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not](https://mlsanity.com/paper/2608.27855/) (2026)
- [Detecting and Guiding LLM-Generated Korean Poetry with Interpretable Form-level Features](https://mlsanity.com/paper/2608.28986/) (2026)

## Abstract (arXiv)

The emergence of large language models (LLMs) capable of generating realistic texts and images has sparked ethical concerns across various sectors. In response, researchers in academia and industry are actively exploring methods to distinguish AI-generated content from human-authored material. However, a crucial question remains: What are the unique characteristics of AI-generated text? Addressing this gap, this study proposes StyloAI, a data-driven model that uses 31 stylometric features to identify AI-generated texts by applying a Random Forest classifier on two multi-domain datasets. StyloAI achieves accuracy rates of 81% and 98% on the test set of the AuTextification dataset and the Education dataset, respectively. This approach surpasses the performance of existing state-of-the-art models and provides valuable insights into the differences between AI-generated and human-authored texts.