RETRACTED ARTICLE: Detection of hate: speech tweets based convolutional neural network and machine learning algorithms
Abstract There is no doubt that social media sites have provided many benefits to humanity, such as sharing information continuously and communicating with others easily. It also seems that social media sites have many advantages, but in addition to these advantages, there are disadvantages that we...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-76632-2 |
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| author | Hameda A. Sennary Ghada Abozaid Ashraf Hemeida Alexey Mikhaylov |
| author_facet | Hameda A. Sennary Ghada Abozaid Ashraf Hemeida Alexey Mikhaylov |
| author_sort | Hameda A. Sennary |
| collection | DOAJ |
| description | Abstract There is no doubt that social media sites have provided many benefits to humanity, such as sharing information continuously and communicating with others easily. It also seems that social media sites have many advantages, but in addition to these advantages, there are disadvantages that we always strive to find a solution. One of these disadvantages is sharing hate speech. In our study, we’re discussing a way to solve this phenomenon by using Term Frequency-Inverse Document Frequency (TF-IDF) based approach to feature engineering on eleven classifiers for machine and deep learning that can automatically identify hate speech. Three different databases were used, the first of which “Hate speech offensive tweets by Davidson et al.”, the second called "Twitter hate speech" and finally we merged the second data with (Cyberbullying dataset (toxicity_parsed_dataset)". The classifiers involved are Logistic Regression (LR), Naive Bayes (NB), Multi-layer Perceptron (MLP), and Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), K-Means, Decision Tree (DT), Gradient Boosting classifier (GBC), and the Extra Trees (ET) in addition to the convolutional neural network (CNN). Maximum accuracy was attained, which exceeded 99%. |
| format | Article |
| id | doaj-art-c2109cb32cf24bd2bc01bfed1e8df31a |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c2109cb32cf24bd2bc01bfed1e8df31a2025-08-20T02:39:40ZengNature PortfolioScientific Reports2045-23222024-11-0114111510.1038/s41598-024-76632-2RETRACTED ARTICLE: Detection of hate: speech tweets based convolutional neural network and machine learning algorithmsHameda A. Sennary0Ghada Abozaid1Ashraf Hemeida2Alexey Mikhaylov3Department of Mathematics, Faculty of Science, Aswan UniversityElectrical Engineering Department, Faculty of Energy Engineering, Aswan UniversityElectrical Engineering Department, Faculty of Energy Engineering, Aswan UniversityDepartment of Financial Technologies, Financial University Under the Government of the Russian FederationAbstract There is no doubt that social media sites have provided many benefits to humanity, such as sharing information continuously and communicating with others easily. It also seems that social media sites have many advantages, but in addition to these advantages, there are disadvantages that we always strive to find a solution. One of these disadvantages is sharing hate speech. In our study, we’re discussing a way to solve this phenomenon by using Term Frequency-Inverse Document Frequency (TF-IDF) based approach to feature engineering on eleven classifiers for machine and deep learning that can automatically identify hate speech. Three different databases were used, the first of which “Hate speech offensive tweets by Davidson et al.”, the second called "Twitter hate speech" and finally we merged the second data with (Cyberbullying dataset (toxicity_parsed_dataset)". The classifiers involved are Logistic Regression (LR), Naive Bayes (NB), Multi-layer Perceptron (MLP), and Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), K-Means, Decision Tree (DT), Gradient Boosting classifier (GBC), and the Extra Trees (ET) in addition to the convolutional neural network (CNN). Maximum accuracy was attained, which exceeded 99%.https://doi.org/10.1038/s41598-024-76632-2 |
| spellingShingle | Hameda A. Sennary Ghada Abozaid Ashraf Hemeida Alexey Mikhaylov RETRACTED ARTICLE: Detection of hate: speech tweets based convolutional neural network and machine learning algorithms Scientific Reports |
| title | RETRACTED ARTICLE: Detection of hate: speech tweets based convolutional neural network and machine learning algorithms |
| title_full | RETRACTED ARTICLE: Detection of hate: speech tweets based convolutional neural network and machine learning algorithms |
| title_fullStr | RETRACTED ARTICLE: Detection of hate: speech tweets based convolutional neural network and machine learning algorithms |
| title_full_unstemmed | RETRACTED ARTICLE: Detection of hate: speech tweets based convolutional neural network and machine learning algorithms |
| title_short | RETRACTED ARTICLE: Detection of hate: speech tweets based convolutional neural network and machine learning algorithms |
| title_sort | retracted article detection of hate speech tweets based convolutional neural network and machine learning algorithms |
| url | https://doi.org/10.1038/s41598-024-76632-2 |
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