Sentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybrid LGR approach
Abstract With the growth of social media, people are sharing more content than ever, including X posts that reflect a variety of emotions and opinions. AI-generated synthetic text, known as deepfake text, is used to imitate human writing to disseminate misleading information and fake news. However,...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-10661-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849766486493626368 |
|---|---|
| author | Madiha Khalid Muhammad Faheem Mushtaq Urooj Akram Mejdl Safran Sultan Alfarhood Imran Ashraf |
| author_facet | Madiha Khalid Muhammad Faheem Mushtaq Urooj Akram Mejdl Safran Sultan Alfarhood Imran Ashraf |
| author_sort | Madiha Khalid |
| collection | DOAJ |
| description | Abstract With the growth of social media, people are sharing more content than ever, including X posts that reflect a variety of emotions and opinions. AI-generated synthetic text, known as deepfake text, is used to imitate human writing to disseminate misleading information and fake news. However, as deepfake technology continues to grow, it becomes harder to accurately understand people’s opinions on deepfake posts. Existing sentiment analysis algorithms frequently fail to capture the domain-specific, misleading, and context-sensitive characteristics of deepfake-related content. This study proposes a hybrid deep learning (DL) approach and novel transfer learning (TL)-based feature extraction approach for deepfake posts’ sentiment analysis. The transfer learning-based approach combines the strengths of the hybrid DL technique to capture global and local contextual information. In this study, we compare the proposed approach with a range of machine learning algorithms, as well as, DL techniques for validation. Different feature extraction techniques, such as a bag of words (BOW), term frequency-inverse document frequency (TF-IDF), word embedding features, and novel TL features that combine the LSTM and DT, are used to build the models. The ML models are fine-tuned with extensive hyperparameter tuning to enhance performance and efficiency. The sentiment analysis performance of each applied method is validated using the k-fold cross-validation. The experimental results indicate that the proposed LGR (LSTM+GRU+RNN) approach with novel TL features performs well with a 99% accuracy. The proposed approach helps detect and prevent the spread of deepfake content, keeping people and organizations safe from its negative effects. This study covers a crucial gap in evaluating deepfake-specific social media sentiment by providing a comprehensive, scalable mechanism for monitoring and reducing the effect of fake content online. |
| format | Article |
| id | doaj-art-beba3319d96441aabd7bf22f50ebf663 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-beba3319d96441aabd7bf22f50ebf6632025-08-20T03:04:34ZengNature PortfolioScientific Reports2045-23222025-08-0115112110.1038/s41598-025-10661-3Sentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybrid LGR approachMadiha Khalid0Muhammad Faheem Mushtaq1Urooj Akram2Mejdl Safran3Sultan Alfarhood4Imran Ashraf5Faculty of Computing, The Islamia University of BahawalpurFaculty of Computing, The Islamia University of BahawalpurFaculty of Computing, The Islamia University of BahawalpurResearch Chair of Online Dialogue and Cultural Communication, Department of Computer Science, College of Computer and Information Sciences, King Saud UniversityDepartment of Computer Science, College of Computer and Information Sciences, King Saud UniversityDepartment of Information and Communication Engineering, Yeungnam UniversityAbstract With the growth of social media, people are sharing more content than ever, including X posts that reflect a variety of emotions and opinions. AI-generated synthetic text, known as deepfake text, is used to imitate human writing to disseminate misleading information and fake news. However, as deepfake technology continues to grow, it becomes harder to accurately understand people’s opinions on deepfake posts. Existing sentiment analysis algorithms frequently fail to capture the domain-specific, misleading, and context-sensitive characteristics of deepfake-related content. This study proposes a hybrid deep learning (DL) approach and novel transfer learning (TL)-based feature extraction approach for deepfake posts’ sentiment analysis. The transfer learning-based approach combines the strengths of the hybrid DL technique to capture global and local contextual information. In this study, we compare the proposed approach with a range of machine learning algorithms, as well as, DL techniques for validation. Different feature extraction techniques, such as a bag of words (BOW), term frequency-inverse document frequency (TF-IDF), word embedding features, and novel TL features that combine the LSTM and DT, are used to build the models. The ML models are fine-tuned with extensive hyperparameter tuning to enhance performance and efficiency. The sentiment analysis performance of each applied method is validated using the k-fold cross-validation. The experimental results indicate that the proposed LGR (LSTM+GRU+RNN) approach with novel TL features performs well with a 99% accuracy. The proposed approach helps detect and prevent the spread of deepfake content, keeping people and organizations safe from its negative effects. This study covers a crucial gap in evaluating deepfake-specific social media sentiment by providing a comprehensive, scalable mechanism for monitoring and reducing the effect of fake content online.https://doi.org/10.1038/s41598-025-10661-3DeepfakeTransfer learningDeep learningMachine learningFeature engineeringLexicon sentiment analysis |
| spellingShingle | Madiha Khalid Muhammad Faheem Mushtaq Urooj Akram Mejdl Safran Sultan Alfarhood Imran Ashraf Sentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybrid LGR approach Scientific Reports Deepfake Transfer learning Deep learning Machine learning Feature engineering Lexicon sentiment analysis |
| title | Sentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybrid LGR approach |
| title_full | Sentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybrid LGR approach |
| title_fullStr | Sentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybrid LGR approach |
| title_full_unstemmed | Sentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybrid LGR approach |
| title_short | Sentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybrid LGR approach |
| title_sort | sentiment analysis for deepfake x posts using novel transfer learning based word embedding and hybrid lgr approach |
| topic | Deepfake Transfer learning Deep learning Machine learning Feature engineering Lexicon sentiment analysis |
| url | https://doi.org/10.1038/s41598-025-10661-3 |
| work_keys_str_mv | AT madihakhalid sentimentanalysisfordeepfakexpostsusingnoveltransferlearningbasedwordembeddingandhybridlgrapproach AT muhammadfaheemmushtaq sentimentanalysisfordeepfakexpostsusingnoveltransferlearningbasedwordembeddingandhybridlgrapproach AT uroojakram sentimentanalysisfordeepfakexpostsusingnoveltransferlearningbasedwordembeddingandhybridlgrapproach AT mejdlsafran sentimentanalysisfordeepfakexpostsusingnoveltransferlearningbasedwordembeddingandhybridlgrapproach AT sultanalfarhood sentimentanalysisfordeepfakexpostsusingnoveltransferlearningbasedwordembeddingandhybridlgrapproach AT imranashraf sentimentanalysisfordeepfakexpostsusingnoveltransferlearningbasedwordembeddingandhybridlgrapproach |