Cyberbullying detection for low-resource languages and dialects: Review of the state of the art

Mahmud, Tanjim; Ptaszyński, Michał; Eronen, Juuso; Masui, Fumito

doi:10.1016/j.ipm.2023.103454

Cited by 74 publications

(14 citation statements)

References 82 publications

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“…This process enables the segmentation of text into meaningful linguistic units, such as words or subwords, thereby facilitating further analysis. Our approach to tokenization aligns with previous research on the Chittagonian language, which also emphasized the importance of breaking down text into linguistic units [7].…”

Section: Tokenizationsupporting

confidence: 73%

“…These characters encompass periods, commas, question marks, exclamation marks, hyphens, parentheses, quotation marks, and other nonalphanumeric symbols. While some punctuation may not significantly alter the meaning of a sentence, their presence can impact text classification tasks, including automatic cyberbullying detection, as demonstrated by Mahmud et al [7] in previous research. Their findings suggest that removing punctuation can enhance text classification accuracy, particularly when employing traditional machine learning algorithms.…”

Section: Removing Punctuationsmentioning

confidence: 87%

Section: Related Workmentioning

confidence: 99%

“…Cyberbullying detection has gained considerable attention in recent years owing to the widespread use of social media platforms and online communication channels [7]. Researchers have explored various techniques and methodologies to identify and address cyberbullying among various languages and cultures [7,8,[31][32][33][34][35]. However, while numerous research efforts have introduced solutions to detect cyberbullying in high-resource languages such as English or Japanese, there is a limited number of studies that have extensively addressed cyberbullying detection in the low-resource languages, such as the Bangla language.…”

Section: Related Workmentioning

confidence: 99%

“…Chittagonian, spoken by approximately 13 million people, adds to Bangladesh's rich linguistic tapestry [5]. The widespread use of Unicode across communication devices has enabled individuals, including Chittagonian speakers, to freely express themselves in their native tongues [7,8]. Social media platforms like Facebook, imo, WhatsApp, or various blog services have been embraced by the people of Chittagong, thereby fostering an environment conducive to uninhibited self-expression [9][10][11].…”

Section: Introductionmentioning

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: Tokenizationsupporting

confidence: 73%

Section: Removing Punctuationsmentioning

confidence: 87%