TweepFake: About detecting deepfake tweets.

The recent advances in language modeling significantly improved the generative capabilities of deep neural models: in 2019 OpenAI released GPT-2, a pre-trained language model that can autonomously generate coherent, non-trivial and human-like text samples. Since then, ever more powerful text generat...

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Autores principales:	Tiziano Fagni, Fabrizio Falchi, Margherita Gambini, Antonio Martella, Maurizio Tesconi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f931b7470e03463eb28a4bce3caa5e05
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id	oai:doaj.org-article:f931b7470e03463eb28a4bce3caa5e05
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spelling	oai:doaj.org-article:f931b7470e03463eb28a4bce3caa5e052021-12-02T20:04:05ZTweepFake: About detecting deepfake tweets.1932-620310.1371/journal.pone.0251415https://doaj.org/article/f931b7470e03463eb28a4bce3caa5e052021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251415https://doaj.org/toc/1932-6203The recent advances in language modeling significantly improved the generative capabilities of deep neural models: in 2019 OpenAI released GPT-2, a pre-trained language model that can autonomously generate coherent, non-trivial and human-like text samples. Since then, ever more powerful text generative models have been developed. Adversaries can exploit these tremendous generative capabilities to enhance social bots that will have the ability to write plausible deepfake messages, hoping to contaminate public debate. To prevent this, it is crucial to develop deepfake social media messages detection systems. However, to the best of our knowledge no one has ever addressed the detection of machine-generated texts on social networks like Twitter or Facebook. With the aim of helping the research in this detection field, we collected the first dataset of real deepfake tweets, TweepFake. It is real in the sense that each deepfake tweet was actually posted on Twitter. We collected tweets from a total of 23 bots, imitating 17 human accounts. The bots are based on various generation techniques, i.e., Markov Chains, RNN, RNN+Markov, LSTM, GPT-2. We also randomly selected tweets from the humans imitated by the bots to have an overall balanced dataset of 25,572 tweets (half human and half bots generated). The dataset is publicly available on Kaggle. Lastly, we evaluated 13 deepfake text detection methods (based on various state-of-the-art approaches) to both demonstrate the challenges that Tweepfake poses and create a solid baseline of detection techniques. We hope that TweepFake can offer the opportunity to tackle the deepfake detection on social media messages as well.Tiziano FagniFabrizio FalchiMargherita GambiniAntonio MartellaMaurizio TesconiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251415 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tiziano Fagni Fabrizio Falchi Margherita Gambini Antonio Martella Maurizio Tesconi TweepFake: About detecting deepfake tweets.
description	The recent advances in language modeling significantly improved the generative capabilities of deep neural models: in 2019 OpenAI released GPT-2, a pre-trained language model that can autonomously generate coherent, non-trivial and human-like text samples. Since then, ever more powerful text generative models have been developed. Adversaries can exploit these tremendous generative capabilities to enhance social bots that will have the ability to write plausible deepfake messages, hoping to contaminate public debate. To prevent this, it is crucial to develop deepfake social media messages detection systems. However, to the best of our knowledge no one has ever addressed the detection of machine-generated texts on social networks like Twitter or Facebook. With the aim of helping the research in this detection field, we collected the first dataset of real deepfake tweets, TweepFake. It is real in the sense that each deepfake tweet was actually posted on Twitter. We collected tweets from a total of 23 bots, imitating 17 human accounts. The bots are based on various generation techniques, i.e., Markov Chains, RNN, RNN+Markov, LSTM, GPT-2. We also randomly selected tweets from the humans imitated by the bots to have an overall balanced dataset of 25,572 tweets (half human and half bots generated). The dataset is publicly available on Kaggle. Lastly, we evaluated 13 deepfake text detection methods (based on various state-of-the-art approaches) to both demonstrate the challenges that Tweepfake poses and create a solid baseline of detection techniques. We hope that TweepFake can offer the opportunity to tackle the deepfake detection on social media messages as well.
format	article
author	Tiziano Fagni Fabrizio Falchi Margherita Gambini Antonio Martella Maurizio Tesconi
author_facet	Tiziano Fagni Fabrizio Falchi Margherita Gambini Antonio Martella Maurizio Tesconi
author_sort	Tiziano Fagni
title	TweepFake: About detecting deepfake tweets.
title_short	TweepFake: About detecting deepfake tweets.
title_full	TweepFake: About detecting deepfake tweets.
title_fullStr	TweepFake: About detecting deepfake tweets.
title_full_unstemmed	TweepFake: About detecting deepfake tweets.
title_sort	tweepfake: about detecting deepfake tweets.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f931b7470e03463eb28a4bce3caa5e05
work_keys_str_mv	AT tizianofagni tweepfakeaboutdetectingdeepfaketweets AT fabriziofalchi tweepfakeaboutdetectingdeepfaketweets AT margheritagambini tweepfakeaboutdetectingdeepfaketweets AT antoniomartella tweepfakeaboutdetectingdeepfaketweets AT mauriziotesconi tweepfakeaboutdetectingdeepfaketweets
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TweepFake: About detecting deepfake tweets.

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