Deep Learning Hybrid Models for Multilingual Cyberbullying Detection: Insights from Bangla and Chittagonian Languages

Mahmud, Tanjim; Ptaszynski, Michal; Masui, Fumito

doi:10.1109/iccit60459.2023.10441181

Cited by 28 publications

(2 citation statements)

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“…The TF-IDF method transforms text data into numerical vectors by calculating the importance of terms based on their frequency in each document and rarity across the entire dataset [49,54]. Each document is represented as a vector, where each element corresponds to the TF-IDF score of a term.…”

Section: Tf-idfmentioning

confidence: 99%

“…As far as we are aware, this study is the first to detect cyberbullying using multilingual models in Bangla and Chittagonian languages. One previous study by Mahmud et al [54] also focused on Bangla and Chittagonian languages for cyberbullying detection. Below, we list the key distinctions and conclusions from the earlier research with reference to the present study: 1.…”

Section: Comparison With Previous Studiesmentioning

confidence: 99%

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Exhaustive Study into Machine Learning and Deep Learning Methods for Multilingual Cyberbullying Detection in Bangla and Chittagonian Texts

Mahmud,

Ptaszynski,

Masui

2024

Electronics

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Cyberbullying is a serious problem in online communication. It is important to find effective ways to detect cyberbullying content to make online environments safer. In this paper, we investigated the identification of cyberbullying contents from the Bangla and Chittagonian languages, which are both low-resource languages, with the latter being an extremely low-resource language. In the study, we used both traditional baseline machine learning methods, as well as a wide suite of deep learning methods especially focusing on hybrid networks and transformer-based multilingual models. For the data, we collected over 5000 both Bangla and Chittagonian text samples from social media. Krippendorff’s alpha and Cohen’s kappa were used to measure the reliability of the dataset annotations. Traditional machine learning methods used in this research achieved accuracies ranging from 0.63 to 0.711, with SVM emerging as the top performer. Furthermore, employing ensemble models such as Bagging with 0.70 accuracy, Boosting with 0.69 accuracy, and Voting with 0.72 accuracy yielded promising results. In contrast, deep learning models, notably CNN, achieved accuracies ranging from 0.69 to 0.811, thus outperforming traditional ML approaches, with CNN exhibiting the highest accuracy. We also proposed a series of hybrid network-based models, including BiLSTM+GRU with an accuracy of 0.799, CNN+LSTM with 0.801 accuracy, CNN+BiLSTM with 0.78 accuracy, and CNN+GRU with 0.804 accuracy. Notably, the most complex model, (CNN+LSTM)+BiLSTM, attained an accuracy of 0.82, thus showcasing the efficacy of hybrid architectures. Furthermore, we explored transformer-based models, such as XLM-Roberta with 0.841 accuracy, Bangla BERT with 0.822 accuracy, Multilingual BERT with 0.821 accuracy, BERT with 0.82 accuracy, and Bangla ELECTRA with 0.785 accuracy, which showed significantly enhanced accuracy levels. Our analysis demonstrates that deep learning methods can be highly effective in addressing the pervasive issue of cyberbullying in several different linguistic contexts. We show that transformer models can efficiently circumvent the language dependence problem that plagues conventional transfer learning methods. Our findings suggest that hybrid approaches and transformer-based embeddings can effectively tackle the problem of cyberbullying across online platforms.

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Section: Tf-idfmentioning

confidence: 99%

Section: Comparison With Previous Studiesmentioning

confidence: 99%