This study addresses the critical challenge of detecting DeepFake tweets by
leveraging advanced natural language processing (NLP) techniques to distinguish
between genuine and AI-generated texts. Given the increasing prevalence of
misinformation, our research utilizes the TweepFake dataset to train and
evaluate various machine learning models. The objective is to identify
effective strategies for recognizing DeepFake content, thereby enhancing the
integrity of digital communications. By developing reliable methods for
detecting AI-generated misinformation, this work contributes to a more
trustworthy online information environment.

本研究利用先进的自然语言处理技术来区分真实和人工智能生成的文本，以解决检测 DeepFake 推文的挑战。通过使用 TweepFake 数据集来训练和评估不同的机器学习模型，旨在识别有效的策略，从而提高数字通信的完整性，通过开发可靠的方法来检测人工智能生成的虚假信息，本研究对于构建一个更可信赖的在线信息环境做出了贡献。

深度伪造推文自动检测

Deepfake tweets automatic detection

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.

该研究收集了以 GPT-2 为代表的各种文本生成技术生成的推特信息，形成了 TweepFake 数据集，进一步通过评估 13 种深度伪造文本检测方法提供了针对社交媒体信息的深度伪造检测的基础。