Using generative adversarial network to improve the accuracy of detecting AI-generated tweets
Abstract This paper provides a novel approach using state-of-the-art generative Artificial Intelligence (AI) models to enhance the accuracy of machine learning methods in detecting AI-generated texts; the underlying generative capabilities are used along with ensemble-based learning methods for the...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-11-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-78601-1 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846147740712566784 |
|---|---|
| author | Yang Hui |
| author_facet | Yang Hui |
| author_sort | Yang Hui |
| collection | DOAJ |
| description | Abstract This paper provides a novel approach using state-of-the-art generative Artificial Intelligence (AI) models to enhance the accuracy of machine learning methods in detecting AI-generated texts; the underlying generative capabilities are used along with ensemble-based learning methods for the exact characterization of created text attributes. Four basic steps are involved in the proposed methodology. The first step of the text process is the preprocessing stage itself consisting of several steps for the purification of irrelevant data. These stages include noise removal, text tokenization, removal of stop-words, word normalization, and handling uncommon words. In the next step, feature engineering and text representations are done whereby every preprocessed text is represented by a square matrix. This matrix encapsulates data about word correlations, cooccurrence, and word weights. The third step is Generative Adversarial Network (GAN)-based feature extraction, using a GAN model to extract efficient features in classifying the texts based on their creator type. After that, it turns the discriminator part into a strong feature extraction model. The fourth step is weighted Random Forest (RF)-based detection, with the features extracted by the discriminator of GAN serving as input to the RF-based detection model. This approach has covered the differences between texts generated by a human and that generated by Artificial Intelligence, with a significant improvement of 99.60% average accuracy, representing a 1.5% improvement against comparative methods. |
| format | Article |
| id | doaj-art-51237da9908e49e9a735001d16a219b1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-51237da9908e49e9a735001d16a219b12024-12-01T12:26:59ZengNature PortfolioScientific Reports2045-23222024-11-0114111610.1038/s41598-024-78601-1Using generative adversarial network to improve the accuracy of detecting AI-generated tweetsYang Hui0School of Humanities and Law, Zhengzhou Shengda UniversityAbstract This paper provides a novel approach using state-of-the-art generative Artificial Intelligence (AI) models to enhance the accuracy of machine learning methods in detecting AI-generated texts; the underlying generative capabilities are used along with ensemble-based learning methods for the exact characterization of created text attributes. Four basic steps are involved in the proposed methodology. The first step of the text process is the preprocessing stage itself consisting of several steps for the purification of irrelevant data. These stages include noise removal, text tokenization, removal of stop-words, word normalization, and handling uncommon words. In the next step, feature engineering and text representations are done whereby every preprocessed text is represented by a square matrix. This matrix encapsulates data about word correlations, cooccurrence, and word weights. The third step is Generative Adversarial Network (GAN)-based feature extraction, using a GAN model to extract efficient features in classifying the texts based on their creator type. After that, it turns the discriminator part into a strong feature extraction model. The fourth step is weighted Random Forest (RF)-based detection, with the features extracted by the discriminator of GAN serving as input to the RF-based detection model. This approach has covered the differences between texts generated by a human and that generated by Artificial Intelligence, with a significant improvement of 99.60% average accuracy, representing a 1.5% improvement against comparative methods.https://doi.org/10.1038/s41598-024-78601-1Artificial IntelligenceAI-generated tweetsGenerative adversarial networkRandom forestText analysis |
| spellingShingle | Yang Hui Using generative adversarial network to improve the accuracy of detecting AI-generated tweets Scientific Reports Artificial Intelligence AI-generated tweets Generative adversarial network Random forest Text analysis |
| title | Using generative adversarial network to improve the accuracy of detecting AI-generated tweets |
| title_full | Using generative adversarial network to improve the accuracy of detecting AI-generated tweets |
| title_fullStr | Using generative adversarial network to improve the accuracy of detecting AI-generated tweets |
| title_full_unstemmed | Using generative adversarial network to improve the accuracy of detecting AI-generated tweets |
| title_short | Using generative adversarial network to improve the accuracy of detecting AI-generated tweets |
| title_sort | using generative adversarial network to improve the accuracy of detecting ai generated tweets |
| topic | Artificial Intelligence AI-generated tweets Generative adversarial network Random forest Text analysis |
| url | https://doi.org/10.1038/s41598-024-78601-1 |
| work_keys_str_mv | AT yanghui usinggenerativeadversarialnetworktoimprovetheaccuracyofdetectingaigeneratedtweets |