# Implementing BERT and fine-tuned RobertA to detect AI generated news by ChatGPT

arXiv: https://arxiv.org/abs/2306.07401 · HTML: https://mlsanity.com/paper/2306.07401/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [BERT with History Answer Embedding for Conversational Question Answering](https://mlsanity.com/paper/1905.05412/) (2019)
- [On the use of BERT for Neural Machine Translation](https://mlsanity.com/paper/1909.12744/) (2019)
- [RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media](https://mlsanity.com/paper/2002.12683/) (2020)
- [Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Media](https://mlsanity.com/paper/2105.10272/) (2021)

## Built on by (most cited first, 13 of 13)

- [WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models](https://mlsanity.com/paper/2311.07138/) (2023)
- [Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges](https://mlsanity.com/paper/2403.18249/) (2024)
- [RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns](https://mlsanity.com/paper/2508.13152/) (2025)
- [From Deception to Detection: The Dual Roles of Large Language Models in Fake News](https://mlsanity.com/paper/2409.17416/) (2024)
- [Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection](https://mlsanity.com/paper/2402.00412/) (2024)
- [Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities](https://mlsanity.com/paper/2405.00711/) (2024)
- [Have LLMs Reopened the Pandora's Box of AI-Generated Fake News?](https://mlsanity.com/paper/2410.19250/) (2024)
- [AISPACE at SemEval-2024 task 8: A Class-balanced Soft-voting System for Detecting Multi-generator Machine-generated Text](https://mlsanity.com/paper/2404.00950/) (2024)
- [Tracing Thought: Using Chain-of-Thought Reasoning to Identify the LLM Behind AI-Generated Text](https://mlsanity.com/paper/2504.16913/) (2025)
- [Measuring Human Involvement in AI-Generated Text: A Case Study on Academic Writing](https://mlsanity.com/paper/2506.03501/) (2025)
- [From Perceptions To Evidence: Detecting AI-Generated Content In Turkish News Media With A Fine-Tuned Bert Classifier](https://mlsanity.com/paper/2602.13504/) (2026)
- [MD-ProTector: Positioning Multiple Data-Driven Prototypes for LLM-Generated Text Detection](https://mlsanity.com/paper/2608.10459/) (2026)
- [AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not](https://mlsanity.com/paper/2608.27855/) (2026)

## Abstract (arXiv)

The abundance of information on social media has increased the necessity of accurate real-time rumour detection. Manual techniques of identifying and verifying fake news generated by AI tools are impracticable and time-consuming given the enormous volume of information generated every day. This has sparked an increase in interest in creating automated systems to find fake news on the Internet. The studies in this research demonstrate that the BERT and RobertA models with fine-tuning had the best success in detecting AI generated news. With a score of 98%, tweaked RobertA in particular showed excellent precision. In conclusion, this study has shown that neural networks can be used to identify bogus news AI generation news created by ChatGPT. The RobertA and BERT models' excellent performance indicates that these models can play a critical role in the fight against misinformation.