About: Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint http://eprints.org/ontology/EPrint Article Article Academic Article Academic Article
seeAlso	HTML Summary of #93628 Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators HTML Summary of #93628 Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators
http://eprints.org/ontology/hasAccepted	Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators (PDF) Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators (PDF)
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dc:hasVersion	Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators (PDF) Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators (PDF)
Title	Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2022 2022
Creator	Jason R. C. Nurse Jason R. C. Nurse Keenan Jones Keenan Jones Shujun Li Shujun Li
status	forthcoming forthcoming peer reviewed peer reviewed
Publisher	Association for the Advancement of Artificial Intelligence Association for the Advancement of Artificial Intelligence
abstract	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. 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.
Is Part Of	https://kar.kent.ac.uk/id/repository https://kar.kent.ac.uk/id/repository
Subject	QA 76 Software, computer programming, QA 76 Software, computer programming, QA76.87 Neural computers, neural networks QA76.87 Neural computers, neural networks T Technology T Technology
list of authors	https://kar.kent.ac.uk/id/eprint/93628#authors https://kar.kent.ac.uk/id/eprint/93628#authors
presented at	16th International AAAI Conference on Web and Social Media (ICWSM-22) 16th International AAAI Conference on Web and Social Media (ICWSM-22)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #93628 Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators HTML Summary of #93628 Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators

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