  Recently, there has been a rise in the development of powerful pre-trained
natural language models, including GPT-2, Grover, and XLM. These models have
shown state-of-the-art capabilities towards a variety of different NLP tasks,
including question answering, content summarisation, and text generation.
Alongside this, there have been many studies focused on online authorship
attribution (AA). That is, the use of models to identify the authors of online
texts. Given the power of natural language models in generating convincing
texts, this paper examines the degree to which these language models can
generate texts capable of deceiving online AA models. Experimenting with both
blog and Twitter data, we utilise GPT-2 language models to generate texts using
the existing posts of online users. We then examine whether these AI-based text
generators are capable of mimicking authorial style to such a degree that they
can deceive typical AA models. From this, we find that current AI-based text
generators are able to successfully mimic authorship, showing capabilities
towards this on both datasets. Our findings, in turn, highlight the current
capacity of powerful natural language models to generate original online posts
capable of mimicking authorial style sufficiently to deceive popular AA
methods; a key finding given the proposed role of AA in real world applications
such as spam-detection and forensic investigation.
