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

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%.

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Publication Details

Journal
Engineering Technology & Applied Science Research
Published
2024-10-09
DOI
https://doi.org/10.48084/etasr.8249
Citations
3
Primary Topic
Advanced Text Analysis Techniques
Type
article
Field-Weighted Citation Impact
1.04
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article

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

Jalal S. Alowibdi, Noura Saad Alghamdi
3 citations
Engineering Technology & Applied Science Research
Advanced Text Analysis Techniques
1.04
article

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

Jalal S. Alowibdi, Noura Saad Alghamdi
article en
3 citations

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%.

Engineering Technology & Applied Science ResearchVol. 14(5)
University of Jeddah (SA)
Quality Education
Openalex Percentile: Top 19%
Advanced Text Analysis Techniques
1.04
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