@inproceedings{valdez-gomez-adorno-2025-text,
title = "Text Graph Neural Networks for Detecting {AI}-Generated Content",
author = "Valdez, Andric and
Gomez-Adorno, Helena",
editor = "Alam, Firoj and
Nakov, Preslav and
Habash, Nizar and
Gurevych, Iryna and
Chowdhury, Shammur and
Shelmanov, Artem and
Wang, Yuxia and
Artemova, Ekaterina and
Kutlu, Mucahid and
Mikros, George",
booktitle = "Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "International Conference on Computational Linguistics",
url = "https://aclanthology.org/2025.genaidetect-1.10/",
pages = "134--139",
abstract = "The widespread availability of Large Language Models (LLMs) such as GPT-4 and Llama-3, among others, has led to a surge in machine-generated content across various platforms, including social media, educational tools, and academic settings. While these models demonstrate remarkable capabilities in generating coherent text, their misuse raises significant concerns. For this reason, detecting machine-generated text has become a pressing need to mitigate these risks. This research proposed a novel classification method combining text-graph representations with Graph Neural Networks (GNNs) and different node feature initialization strategies to distinguish between human-written and machine-generated content. Experimental results demonstrate that the proposed approach outperforms traditional machine learning classifiers, highlighting the effectiveness of integrating structural and semantic relationships in text."
}
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%0 Conference Proceedings
%T Text Graph Neural Networks for Detecting AI-Generated Content
%A Valdez, Andric
%A Gomez-Adorno, Helena
%Y Alam, Firoj
%Y Nakov, Preslav
%Y Habash, Nizar
%Y Gurevych, Iryna
%Y Chowdhury, Shammur
%Y Shelmanov, Artem
%Y Wang, Yuxia
%Y Artemova, Ekaterina
%Y Kutlu, Mucahid
%Y Mikros, George
%S Proceedings of the 1stWorkshop on GenAI Content Detection (GenAIDetect)
%D 2025
%8 January
%I International Conference on Computational Linguistics
%C Abu Dhabi, UAE
%F valdez-gomez-adorno-2025-text
%X The widespread availability of Large Language Models (LLMs) such as GPT-4 and Llama-3, among others, has led to a surge in machine-generated content across various platforms, including social media, educational tools, and academic settings. While these models demonstrate remarkable capabilities in generating coherent text, their misuse raises significant concerns. For this reason, detecting machine-generated text has become a pressing need to mitigate these risks. This research proposed a novel classification method combining text-graph representations with Graph Neural Networks (GNNs) and different node feature initialization strategies to distinguish between human-written and machine-generated content. Experimental results demonstrate that the proposed approach outperforms traditional machine learning classifiers, highlighting the effectiveness of integrating structural and semantic relationships in text.
%U https://aclanthology.org/2025.genaidetect-1.10/
%P 134-139
Markdown (Informal)
[Text Graph Neural Networks for Detecting AI-Generated Content](https://aclanthology.org/2025.genaidetect-1.10/) (Valdez & Gomez-Adorno, GenAIDetect 2025)
ACL