mlsanityData to 7 Oct 2026

Quantitative Analysis of AI-Generated Texts in Academic Research: A Study of AI Presence in Arxiv Submissions using AI Detection Tool

arXiv 2403.13812 ↗First posted 9 Feb 2024cs.DL cs.AI cs.CLcited by 3

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Abstract (arXiv)

Many people are interested in ChatGPT since it has become a prominent AIGC model that provides high-quality responses in various contexts, such as software development and maintenance. Misuse of ChatGPT might cause significant issues, particularly in public safety and education, despite its immense potential. The majority of researchers choose to publish their work on Arxiv. The effectiveness and originality of future work depend on the ability to detect AI components in such contributions. To address this need, this study will analyze a method that can see purposely manufactured content that academic organizations use to post on Arxiv. For this study, a dataset was created using physics, mathematics, and computer science articles. Using the newly built dataset, the following step is to put originality.ai through its paces. The statistical analysis shows that Originality.ai is very accurate, with a rate of 98%.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 23 references that are in the map. First posted on arXiv 9 Feb 2024.

Builds on

4 of 23 references.

References · 4

  1. 2020COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twittercited by 96
  2. 2023Testing of Detection Tools for AI-Generated Textcited by 86
  3. 2020Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancementscited by 17
  4. 2023An Empirical Study of AI Generated Text Detection Toolscited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.