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With the development of generative models like GPT-3, it is increasingly morechallenging to differentiate generated texts from human-written ones. There isa large number of studies that have demonstrated good results in botidentification. However, the majority of such works depend on supervisedlearning methods that require labelled data and/or prior knowledge about thebot-model architecture. In this work, we propose a bot identification algorithmthat is based on unsupervised learning techniques and does not depend on alarge amount of labelled data. By combining findings in semantic analysis byclustering (crisp and fuzzy) and information techniques, we construct a robustmodel that detects a generated text for different types of bot. We find thatthe generated texts tend to be more chaotic while literary works are morecomplex. We also demonstrate that the clustering of human texts results infuzzier clusters in comparison to the more compact and well-separated clustersof bot-generated texts.

Spot the Bot: Distinguishing Human-Written and Bot-Generated Texts Using Clustering and Information Theory Techniques