mlsanityData to 7 Oct 2026

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

arXiv 2306.07401 ↗First posted 9 Jun 2023cs.CL cs.AIcited by 13

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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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 6 of its 38 references that are in the map. First posted on arXiv 9 Jun 2023.

Builds on

6 of 38 references.

References · 6

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 334
  3. 2019BERT with History Answer Embedding for Conversational Question Answeringcited by 67
  4. 2019On the use of BERT for Neural Machine Translationcited by 23
  5. 2021Stance Detection with BERT Embeddings for Credibility Analysis of Information on Social Mediacited by 5
  6. 2020RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Mediacited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 3; 2026: 3.

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  1. 2024Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challengescited by 28
  2. 2023WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Modelscited by 27
  3. 2025RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patternscited by 24
  4. 2024From Deception to Detection: The Dual Roles of Large Language Models in Fake Newscited by 11
  5. 2024Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detectioncited by 10
  6. 2024Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunitiescited by 7
  7. 2024Have LLMs Reopened the Pandora's Box of AI-Generated Fake News?cited by 6
  8. 2024AISPACE at SemEval-2024 task 8: A Class-balanced Soft-voting System for Detecting Multi-generator Machine-generated Textcited by 3
  9. 2025Tracing Thought: Using Chain-of-Thought Reasoning to Identify the LLM Behind AI-Generated Textcited by 3
  10. 2025Measuring Human Involvement in AI-Generated Text: A Case Study on Academic Writingcited by 0
  11. 2026From Perceptions To Evidence: Detecting AI-Generated Content In Turkish News Media With A Fine-Tuned Bert Classifiercited by 0
  12. 2026MD-ProTector: Positioning Multiple Data-Driven Prototypes for LLM-Generated Text Detectioncited by 0
1 more
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