1. How does cyberbullying affect victims?
Cyberbullying affects victims physically and mentally, leading to trauma. The trauma caused by cyberbullying can result in self-harm, such as suicide. Therefore, detecting and preventing cyberbullying is crucial for the protection of young people. The research presented in the introduction discusses a machine learning-based cyberbullying detection model that can determine whether communication is related to cyberbullying. Various machine learning methods, such as Naive Bayes, Support Vector Machines, Decision Trees, and Random Forests, are explored in cyberbullying detection models. The performance analysis is conducted using two independent functions: BoW and TF-IDF. The findings show that TF-IDF features outperform BoW in terms of accuracy, while SVM outperforms the method used in this article. The increasing use of social media has led to the emergence of cyberbullying, which can be devastating for victims. The research aims to develop a deep learning model using a short-term ad hoc (LSTM) network to identify bullying patterns in social media. The project involves data collection, data preparation, and design and site evaluation. It also highlights the importance of addressing cyberbullying in the digital world.
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2. What models perform best in Roman Urdu text?
RNN-LSTM and RNN-BiLSTM models perform best in Roman Urdu text. These models were tested using RNN-LSTM, RNN-BiLSTM, and CNN models. The BiGRU-CNN classification theory model was also published for cyberbullying detection. The models were evaluated using various criteria such as accuracy, precision, recall, and F1 score. On the test set, the model achieved 95.6% accuracy, showing that it can identify the nature of cyberbullying in social media messages. The training model is stored and can now be used to predict new data. The initiative can be used as a tool to prevent cyberbullying and help victims. Gated Recurrent Units (GRUs) were found to be the most efficient among all algorithms used in this study, with an accuracy rate of 95.47%.
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3. What is the purpose of the proposed cyberbullying system?
The purpose of the proposed cyberbullying system is to design and develop a system that can identify and analyze the nature of online bullying by social media users. It aims to overcome past detection shortcomings and provide effective training models and word usage patterns. The system facilitates cyberbullying rate analysis on various social media sites and implements necessary protection. The planning process involves data collection, pre-processing, LSTM model design and training, and evaluation using parameters like accuracy, precision, recall, and F1 score. After evaluation, the model can detect cyberbullying in real environments and be extended for broader monitoring and action.
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