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