The threat of deepfakes, synthetic, or manipulated media, is becoming increasingly alarming, especially for social media platforms that have already been accused of manipulating public opinion. Even the cheapest text generation techniques (e. g. the search-and-replace method) can deceive humans, as the Net Neutrality scandal proved in 2017. Meanwhile, more powerful generative models have been released, from RNN-based methods to the GPT-2 language model. State-of-the-art language models, transformer-based in particular, can generate synthetic text in response to the model being primed with arbitrary input. Thus, Therefore, it is crucial to develop tools that help to detect media authenticity. To help the research in this  field, we collected a dataset of real Deepfake tweets\real  . 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, 836 tweets (half human and half bots generated). The dataset is publicly available on Kaggle. In order to create a solid baseline for detection techniques on the proposed dataset we tested 13 detection methods based on various state-of-the-art approaches . The detection results reported as a baseline using 13 detection methods, confirm that the newest and more sophisticated generative methods based on transformer architecture (e. g., GPT-2) can produce high-quality short texts, difficult to detect .
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 .
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threat of deepfakes, synthetic, or manipulated media, is becoming increasingly alarming, especially for social media platforms that have already been accused of manipulating public opinion. Even the cheapest text generation techniques (e. g. the search-and-replace method) can deceive humans, as the Net Neutrality scandal proved in 2017. Meanwhile, more powerful
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
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released, from RNN-based methods to the GPT-2 language model. State-of-the-art language models, transformer-based in particular, can generate synthetic text in response to the model being primed with arbitrary input. Thus, Therefore,
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,
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tools that help to detect media authenticity. To help the
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
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detection
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a dataset of real Deepfake tweets
the first dataset of
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deepfake tweets, TweepFake
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836
572
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In order to create a solid baseline for detection techniques on the proposed dataset we tested
Lastly, we evaluated
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detection methods
deepfake text detection methods (
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. The detection results reported as a baseline using 13 detection methods, confirm that the newest and more sophisticated generative methods based on transformer architecture (e. g., GPT-2) can produce high-quality short texts, difficult to detect
) 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