@inproceedings{ashraf-etal-2025-inside,
title = "Inside the Box: A Streamlined Model for {AI}-Generated News Article Detection",
author = "Ashraf, Nsrin and
Labib, Mariam and
Nayel, Hamada",
editor = "Lamsiyah, Salima and
Ezzini, Saad and
El Mahdaoui, Abdelkader and
Alami, Hamza and
Benlahbib, Abdessamad and
El Amrani, Samir and
Chafik, Salmane and
Hammouchi, Hicham",
booktitle = "Proceedings of the Shared Task on Multi-Domain Detection of AI-Generated Text",
month = sep,
year = "2025",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2025.ranlp-mdaigt.5/",
pages = "26--30",
abstract = "The rapid proliferation of AI-generated text has raised concerns. With the increasing prevalence of AI-generated content, concerns have grown regarding authenticity, authorship, and the spread of misinformation. Detecting such content accurately and efficiently has become a pressing challenge. In this study, we propose a simple yet effective system for classifying AI-generated versus human-written text. Rather than relying on complex or resource-intensive deep learning architectures, our approach leverages classical machine learning algorithms combined with the TF-IDF text representation technique. Evaluated on the M-DAIGT shared task dataset, our Support Vector Machine (SVM) based system achieved strong results, ranking second on the official leaderboard and demonstrating competitive performance across all evaluation metrics. These findings highlight the potential of traditional lightweight models to address modern challenges in text authenticity detection, particularly in low-resource or real-time applications where interpretability and efficiency are essential."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ashraf-etal-2025-inside">
<titleInfo>
<title>Inside the Box: A Streamlined Model for AI-Generated News Article Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nsrin</namePart>
<namePart type="family">Ashraf</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mariam</namePart>
<namePart type="family">Labib</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hamada</namePart>
<namePart type="family">Nayel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-09</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Shared Task on Multi-Domain Detection of AI-Generated Text</title>
</titleInfo>
<name type="personal">
<namePart type="given">Salima</namePart>
<namePart type="family">Lamsiyah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saad</namePart>
<namePart type="family">Ezzini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abdelkader</namePart>
<namePart type="family">El Mahdaoui</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hamza</namePart>
<namePart type="family">Alami</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Abdessamad</namePart>
<namePart type="family">Benlahbib</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Samir</namePart>
<namePart type="family">El Amrani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Salmane</namePart>
<namePart type="family">Chafik</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hicham</namePart>
<namePart type="family">Hammouchi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
<place>
<placeTerm type="text">Varna, Bulgaria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>The rapid proliferation of AI-generated text has raised concerns. With the increasing prevalence of AI-generated content, concerns have grown regarding authenticity, authorship, and the spread of misinformation. Detecting such content accurately and efficiently has become a pressing challenge. In this study, we propose a simple yet effective system for classifying AI-generated versus human-written text. Rather than relying on complex or resource-intensive deep learning architectures, our approach leverages classical machine learning algorithms combined with the TF-IDF text representation technique. Evaluated on the M-DAIGT shared task dataset, our Support Vector Machine (SVM) based system achieved strong results, ranking second on the official leaderboard and demonstrating competitive performance across all evaluation metrics. These findings highlight the potential of traditional lightweight models to address modern challenges in text authenticity detection, particularly in low-resource or real-time applications where interpretability and efficiency are essential.</abstract>
<identifier type="citekey">ashraf-etal-2025-inside</identifier>
<location>
<url>https://aclanthology.org/2025.ranlp-mdaigt.5/</url>
</location>
<part>
<date>2025-09</date>
<extent unit="page">
<start>26</start>
<end>30</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Inside the Box: A Streamlined Model for AI-Generated News Article Detection
%A Ashraf, Nsrin
%A Labib, Mariam
%A Nayel, Hamada
%Y Lamsiyah, Salima
%Y Ezzini, Saad
%Y El Mahdaoui, Abdelkader
%Y Alami, Hamza
%Y Benlahbib, Abdessamad
%Y El Amrani, Samir
%Y Chafik, Salmane
%Y Hammouchi, Hicham
%S Proceedings of the Shared Task on Multi-Domain Detection of AI-Generated Text
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F ashraf-etal-2025-inside
%X The rapid proliferation of AI-generated text has raised concerns. With the increasing prevalence of AI-generated content, concerns have grown regarding authenticity, authorship, and the spread of misinformation. Detecting such content accurately and efficiently has become a pressing challenge. In this study, we propose a simple yet effective system for classifying AI-generated versus human-written text. Rather than relying on complex or resource-intensive deep learning architectures, our approach leverages classical machine learning algorithms combined with the TF-IDF text representation technique. Evaluated on the M-DAIGT shared task dataset, our Support Vector Machine (SVM) based system achieved strong results, ranking second on the official leaderboard and demonstrating competitive performance across all evaluation metrics. These findings highlight the potential of traditional lightweight models to address modern challenges in text authenticity detection, particularly in low-resource or real-time applications where interpretability and efficiency are essential.
%U https://aclanthology.org/2025.ranlp-mdaigt.5/
%P 26-30
Markdown (Informal)
[Inside the Box: A Streamlined Model for AI-Generated News Article Detection](https://aclanthology.org/2025.ranlp-mdaigt.5/) (Ashraf et al., RANLP 2025)
ACL