Detecting and unmasking AI-generated texts through explainable artificial intelligence using stylistic features
Open accessAbstract
In recent years, Artificial Intelligence (AI) has sig-nificantly transformed various aspects of human activities, including text composition. The advancements in AI technology have enabled computers to generate text that closely mimics human writing which is raising concerns about misinformation, identity theft, and security vulnerabilities. To address these challenges, understanding the underlying patterns of AI-generated text is essential. This research focuses on uncovering these patterns to establish ethical guidelines for distinguishing between AI-generated and human-generated text. This research contributes to the ongoing discourse on AI-generated content by elucidating methodologies for distinguishing between human and machine-generated text. The research delves into parameters such as syllable count, word length, sentence structure, functional word usage, and punctuation ratios to detect AI-generated text. Furthermore, the research integrates Explainable AI (xAI) techniques—LIME and SHAP—to enhance the interpretability of machine learning model predictions. The model demonstrated excellent efficacy, showing an accuracy of 93%.Leveraging xAI techniques, further uncovering that pivotal attributes such as Herdan’s C, MaaS, and Simpson’s Index played a dominant role in the classification process.
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Shah et al. (2023) constructed a classifier based on stylistic features such as syllable count, word length, sentence structure, frequency of function word usage, and punctuation ratio, achieving an accuracy of 93%.
“Shah et al. (2023) constructed a classifier based on stylistic features such as syllable count, word length, sentence structure, frequency of function word usage, and punctuation ratio. This classifier achieved an accuracy of 93%”
From A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions (2025) · cites this paper as Shah et al. 2023
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- Authors
- Shah, A., Ranka, P., Dedhia, U., Prasad, S., Muni, S., & Bhowmick, K.
- Affiliations
- Dwarkadas J. Sanghvi College of Engineering (IN)
- Venue
- International Journal of Advanced Computer Science and Applications
- Published
- 2023
- Volume
- 14
- Issue
- 10
- ISSN
- 2156-5570 (ISSN-L)
- 2158-107X
- OpenAlex
- W4388144893
- Cited by
- 47
- References
- 38
- Citations by year
- 2026: 14 · 2025: 23 · 2024: 10
- FWCI
- 4.43
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Advanced Computer Science and Applications (publishedVersion, cc-by)
- International Journal of Advanced Computer Science and Applications (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- AI-generated text detection, stylistic features, explainable AI, lime, SHAP, Meuse River, Simpson index, syllable count, word length, sentence structure, misinformation
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