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During the last two decades, we have progressively turned to the Internet andsocial media to find news, entertain conversations and share opinion. Recently,OpenAI has developed a ma-chine learning system called GPT-2 for GenerativePre-trained Transformer-2, which can pro-duce deepfake texts. It can generateblocks of text based on brief writing prompts that look like they were writtenby humans, facilitating the spread false or auto-generated text. In line withthis progress, and in order to counteract potential dangers, several methodshave been pro-posed for detecting text written by these language models. Inthis paper, we propose a transfer learning based model that will be able todetect if an Arabic sentence is written by humans or automatically generated bybots. Our dataset is based on tweets from a previous work, which we havecrawled and extended using the Twitter API. We used GPT2-Small-Arabic togenerate fake Arabic Sentences. For evaluation, we compared different recurrentneural network (RNN) word embeddings based baseline models, namely: LSTM,BI-LSTM, GRU and BI-GRU, with a transformer-based model. Our newtransfer-learning model has obtained an accuracy up to 98%. To the best of ourknowledge, this work is the first study where ARABERT and GPT2 were combined todetect and classify the Arabic auto-generated texts.

BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets