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