Distinguishing Arabic GenAI-generated Tweets and Human Tweets utilizing Machine Learning

Authors

  • Noura Saad Alghamdi Computer Science Department, University of Jeddah, Jeddah, Saudi Arabia
  • Jalal Suliman Alowibdi Computer Science Department, University of Jeddah, Jeddah, Saudi Arabia
Volume: 14 | Issue: 5 | Pages: 16720-16726 | October 2024 | https://doi.org/10.48084/etasr.8249

Abstract

Generative Artificial Intelligence (GenAI) tools, like ChatGPT, have made it easy to create text, music, images, and other types of media. GenAI, a type of AI technology, has rapidly gained fame and popularity for its ability to generate new content. Notably, its applications allow anyone to produce natural conversations and content, making it increasingly challenging to distinguish between human-written and GenAI-generated material. The current research focuses on Arabic content to differentiate GenAI-generated content from authentic human-written content on the X platform (Twitter). Datasets from both real human-written tweets and GenAI-generated tweets were collected. Then, three Machine Learning models were built to predict whether a tweet source is GenAI-generated or human-written. The highest achieved accuracy was 93%.

Keywords:

Twitter, GenAI, Machine Learning (ML), OpenAI, ChatGPT

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References

H. Yu and Y. Guo, "Generative artificial intelligence empowers educational reform: current status, issues, and prospects," Frontiers in Education, vol. 8, Jun. 2023, Art. no. 1183162.

I. Augenstein et al., "Factuality Challenges in the Era of Large Language Models." arXiv, Oct. 09, 2023.

R. Shijaku and E. Canhasi, "ChatGPT Generated Text Detection," Jan. 2023.

I. Katib, F. Y. Assiri, H. A. Abdushkour, D. Hamed, and M. Ragab, "Differentiating Chat Generative Pretrained Transformer from Humans: Detecting ChatGPT-Generated Text and Human Text Using Machine Learning," Mathematics, vol. 11, no. 15, Jan. 2023, Art. no. 3400.

R. Tang, Y.-N. Chuang, and X. Hu, "The Science of Detecting LLM-Generated Text," Communications of the ACM, vol. 67, no. 4, pp. 50–59, Nov. 2024.

N. Islam, D. Sutradhar, H. Noor, J. T. Raya, M. T. Maisha, and D. M. Farid, "Distinguishing Human Generated Text From ChatGPT Generated Text Using Machine Learning." arXiv, May 26, 2023.

Y. Chen, H. Kang, V. Zhai, L. Li, R. Singh, and B. Raj, "GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content." arXiv, May 17, 2023.

N. Islam, D. Sutradhar, H. Noor, J. T. Raya, M. T. Maisha, and D. M. Farid, "Distinguishing Human Generated Text From ChatGPT Generated Text Using Machine Learning." arXiv, May 26, 2023.

C. Vasilatos, M. Alam, T. Rahwan, Y. Zaki, and M. Maniatakos, "HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis." arXiv, Jun. 07, 2023.

S. Chakraborty, A. S. Bedi, S. Zhu, B. An, D. Manocha, and F. Huang, "On the Possibilities of AI-Generated Text Detection." arXiv, Oct. 02, 2023.

K. Hayawi, S. Shahriar, and S. Mathew, "The Imitation Game: Detecting Human and AI-Generated Texts in the Era of Large Language Models," Jul. 2023, [Online]. Available: https://www.researchgate.net/publication/

_The_Imitation_Game_Detecting_Human_and_AI-Generated_Texts_in_the_Era_of_Large_Language_Models.

M. Perkins et al., "GenAI Detection Tools, Adversarial Techniques and Implications for Inclusivity in Higher Education." arXiv, Mar. 28, 2024.

M. Fattah and M. A. Haq, "Tweet Prediction for Social Media using Machine Learning," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14698–14703, Jun. 2024.

M. Madhukar and S. Verma, "Hybrid Semantic Analysis of Tweets: A Case Study of Tweets on Girl-Child in India," Engineering, Technology & Applied Science Research, vol. 7, no. 5, pp. 2014–2016, Oct. 2017.

H. Zhang and H. Shao, "Exploring the Latest Applications of OpenAI and ChatGPT: An In-Depth Survey," Computer Modeling in Engineering & Sciences, vol. 138, no. 3, pp. 2061–2102, 2024.

K. Darwish and H. Mubarak, "Farasa: A New Fast and Accurate Arabic Word Segmenter," in Tenth InternationalConference on Language Resources and Evaluation, Portoroz, Slovenia, Dec. 2016, pp. 1070–1074.

"Word Segmentation Module," Farasa. https://farasa.qcri.org/segmentation/.

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How to Cite

[1]
Alghamdi, N.S. and Alowibdi, J.S. 2024. Distinguishing Arabic GenAI-generated Tweets and Human Tweets utilizing Machine Learning. Engineering, Technology & Applied Science Research. 14, 5 (Oct. 2024), 16720–16726. DOI:https://doi.org/10.48084/etasr.8249.

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