Improving Cyberbullying Detection Through Adaptive External Dictionary in Machine Learning

Qudah, Hamzeh; Alhija, Mwaffaq Abu; Tarawneh, Hassan

doi:10.21203/rs.3.rs-3306599/v1

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…The results show that for the proposed SOSNet, using SBERT, an accuracy score of 92.70% was achieved. In a separate study, Al Qudah et al [22] suggested an improved system for cyberbullying detection utilizing an adaptive external dictionary (AED). The authors employed ML models such as RF, XGB, and CatBoost, and introduced ensemble voting models.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Other studies, such as [23], [25], [26], employed ML models for a similar purpose. Ensemble models were also explored in [20], [22], making a significant contribution to the efficient detection of cyberbullying. The comparative outcomes given in Table 6 demonstrate that the proposed approach yields superior results when compared to these existing approaches.…”

Section: E Performance Comparison With Existing Studiesmentioning

confidence: 99%

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RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features

Jamjoom,

Karamti,

Umer

et al. 2024

IEEE Access

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Online platforms are fostering social interaction, but unfortunately, this has given rise to antisocial behaviors such as cyberbullying, trolling, and hate speech on a global scale. The detection of hate and aggression has become a vital aspect of combating cyberbullying and cyberharassment. Cyberbullying involves using aggressive and offensive language including rude, insulting, hateful, and teasing comments to harm individuals on social media platforms. Human moderation is both slow and expensive, making it impractical in the face of rapidly growing data. Automatic detection systems are essential to curb trolling effectively. This research deals with the challenge of automatically identifying cyberbullying in tweets from a publicly available cyberbullying dataset. This research work employs robustly optimized bidirectional encoder representations from the transformers approach (RoBERTa), utilizing global vectors for word representation (GloVe) word embedding features. The proposed approach is further compared with the state-of-the-art machine, deep, and transformer-based learning approaches with the FastText word embedding approach. Statistical results demonstrate that the proposed model outperforms others, achieving a 95% accuracy for detecting cyberbullying tweets. Results from k-fold cross-validation further affirm the supremacy of the proposed model.

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Section: Literature Reviewmentioning

confidence: 99%

Section: E Performance Comparison With Existing Studiesmentioning

confidence: 99%

RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features

Jamjoom,

Karamti,

Umer

et al. 2024

IEEE Access

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show abstract

NLP-Powered Identification of Online Harassment

Agarwal,

Mittal,

Gupta

et al. 2024

2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT)

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Empowering Digital Civility with an NLP Approach for Detecting 𝕏 (Formerly Known as Twitter) Cyberbullying through Boosted Ensembles

Prabakaran,

Muthunambu,

Jeyaraman

2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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As the number of social networking sites grows, so do cyber dangers. Cyberbullying is harmful behaviour that uses technology to intimidate, harass or harm someone, often on social media platforms like Twitter. Machine learning is the optimal approach for cyberbullying detection on Twitter to process large amounts of data, identify patterns of offensive behaviour, and automate the detection process for corpus of tweets. To identify cyber threats using a trained model, the Boosted Ensemble (BE) technique is assessed with various machine learning algorithms such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), Bidirectional LSTM (BILSTM), Recurrent Neural Network LSTM (RNN-LSTM), Multi-Modal Cyberbullying Detection (MMCD) and Random Forest (RF). These classifiers are trained on the vectorized data to classify the tweets to identify cyberbullying threats. The proposed framework can detect cyberbullying cases precisely on tweets. The significance of the work lies in detecting and mitigating cyber threats in real-time and it impacts in enhancing the safety and well-being of social media users by reducing instances of cyberbullying and other cyber threat. The comparative analysis is done using metrices like accuracy, precision, recall and F1-score and the comparison results shows that boosted ensemble technique outperforms other compared algorithms with its overall performance. The accuracy rate of CNN, LSTM, NB, DT, SVM, RF, BILSTM and BE (92.5%, 93.5%, 84.6%, 88%, 89.3%, 92%, 93.75% and 96%), precision rate of CNN, LSTM, NB, DT, SVM, RF, RNN-LSTM and BE (90.2%, 91.3%, 88%, 85%, 86%, 91.6%, 92.1% and 94%), recall rate of CNN, LSTM, NB, DT, SVM, RF, BILSTM and BE (89.8%, 90.7%, 90%, 82%, 88.67%, 89%, 91.04% and 93.7%) and F1 score of CNN, LSTM, NB, DT, SVM, RF, MMCD and BE (90.6%, 91.8%, 85%, 84.56% 87.2%, 90%, 84.6% and 94.89%).

show abstract

Improving Cyberbullying Detection Through Adaptive External Dictionary in Machine Learning

Cited by 3 publications

References 28 publications

RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features

RoBERTaNET: Enhanced RoBERTa Transformer Based Model for Cyberbullying Detection With GloVe Features

NLP-Powered Identification of Online Harassment

Empowering Digital Civility with an NLP Approach for Detecting 𝕏 (Formerly Known as Twitter) Cyberbullying through Boosted Ensembles

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