Deepfake tweets automatic detection
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 18 references that are in the map. First posted on arXiv 24 Jun 2024.
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References · 10
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
- 2019GLTR: Statistical Detection and Visualization of Generated Textcited by 490
- 2019Automatic Detection of Generated Text is Easiest when Humans are Fooledcited by 260
- 2020TweepFake: about Detecting Deepfake Tweetscited by 117
- 2019Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detectioncited by 50
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