Distinguishing Human from Bot Texts: A Graph-Based and Few-Shot Learning Approach
محورهای موضوعی : Natural Language Processing
Ohood Al Minshidawi
1
,
Abdol-Hossein Vahabie
2
1 - Computer engineering department, college of Alborz, University of Tehran, Tehran, Iran
2 - Tehran University
کلید واژه: Arabic text bot detection, Graph neural networks, Graph attention networks, Graph convolutional networks, SetFit model,
چکیده مقاله :
Bots are automated programs created to carry out specific tasks on the internet. On social networks, bots frequently disseminate automated or misleading content, posing significant challenges to the integrity and reliability of these platforms. Detecting and mitigating bot activity is crucial for maintaining the trustworthiness of social media environments. This task becomes even more challenging in low-resource languages like Arabic, where intricate linguistic structures and limited annotated datasets complicate accurate classification. In this regard, a novel framework is introduced for distinguishing between human- and bot-generated Arabic text, using the AutoTweet-Dataset. The framework evaluates two categories of advanced models: graph neural networks and the SetFit model. The first category encompasses two distinct architectures: graph attention networks and graph convolutional networks. In contrast, the SetFit model leverages few-shot learning to facilitate efficient classification. Besides creating an advanced model for identifying bot-generated text, our primary objective is to compare graph neural network-based models with the SetFit model in addressing the complexities of Arabic text processing. The evaluation results determine that the SetFit model achieved the highest accuracy at 88.35%, demonstrating its effectiveness in differentiating between text generated by humans and bots. This research represents a significant advancement in bot detection techniques for low-resource languages. Introducing scalable and efficient methodologies enhances the accuracy of automated content detection, contributing to the security and authenticity of social media interactions in the face of increasingly advanced bot activity.
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