COVID-19 and cyberbullying: deep ensemble model to identify cyberbullying from code-switched languages during the pandemic

Paul, Sayanta; Saha, Sriparna; Singh, Jyoti

doi:10.1007/s11042-021-11601-9

Cited by 12 publications

(3 citation statements)

References 16 publications

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“…By using an epoch number of 30, in [22], cyberbullying was detected in English-Hindi (En-Hi) code-switched text in an attempt to develop a new code-switched Twitter dataset by machine learning (SVM and logistic regression) and deep learning (multilayer perceptron, CNN, BiLSTM, BERT) algorithms. The deep ensemble model performed well on code-switched data, yielding a state-of-the-art F1-score of 0.93.…”

Section: Resultsmentioning

confidence: 99%

Cyberbullying Detection and Severity Determination Model

Obaid,

Guirguis,

Elkaffas

2023

IEEE Access

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Some teenagers actively participate in cyberbullying, which is a pattern of online harassment of others. Many teenagers are unaware of the risks posed by cyberbullying, which can include depression, self-harm, and suicide. Because of the serious harm it can cause to a person's mental health, cyberbullying is an important problem that needs to be addressed. This research aimed to develop a technique to identify the severity of bullying using a deep learning algorithm and fuzzy logic. In this task, Twitter data (47,733 comments) from Kaggle were processed and analyzed to flag cyberbullying comments. The comments embedded by Keras were fed into a long short-term memory network, composed of four layers, for classification. After that, fuzzy logic was applied to determine the severity of the comments. Experimental results suggest that the proposed framework provides a suitable solution to detect bulling with values of 93.67%, 93.64%, 93.62% achieved for the accuracy, F1-score, and recall, respectively.INDEX TERMS Deep learning algorithm, severity of bullying, LSTM.

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Section: Resultsmentioning

confidence: 99%

Cyberbullying Detection and Severity Determination Model

Obaid,

Guirguis,

Elkaffas

2023

IEEE Access

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“…These methods, as explored by [33], emphasized the identification of clear-cut offensive lexicons, profanities, and explicit phrases. However, they are notoriously deficient in handling sophisticated language constructs, sarcasm, or contextually offensive content, resulting in high false-positive rates [34].…”

Section: Traditional Methods In Offensive Language Detectionmentioning

confidence: 99%

Offensive Language Detection on Online Social Networks using Hybrid Deep Learning Architecture

Kazbekova,

Ismagulova,

Kemelbekova

et al. 2023

IJACSA

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In the digital era, online social networks (OSNs) have revolutionized communication, creating spaces for vibrant public discourse. However, these platforms also harbor offensive language that can proliferates hate speech, cyberbullying, and discrimination, significantly undermining the quality of online interactions and posing severe social implications. This research paper introduces a sophisticated approach to offensive language detection on OSNs, employing a novel Hybrid Deep Learning Architecture (HDLA). The urgency of addressing offensive content is juxtaposed with the challenges inherent in accurately identifying nuanced communications, thus necessitating an advanced model that transcends the limitations of traditional natural language processing techniques. The proposed HDLA model synergistically integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks, capitalizing on the strengths of both methodologies. While the CNN component excels in the hierarchical extraction of spatial features within text data, identifying offensive patterns often concealed in the structural nuances, the LSTM network, adept in processing sequential data, captures the contextual dependencies in user posts over time. This duality ensures a comprehensive analysis of complex linguistic constructs, enhancing the detection accuracy for both overt and covert offensive content. Our research meticulously evaluates the HDLA model using extensive, multi-source datasets reflective of diverse OSN environments, establishing benchmarks against prevailing deep learning models. Results indicate a substantial improvement in precision, recall, and F1-score, demonstrating the model's efficacy in identifying offensive language amidst varying degrees of subtlety and complexity. Furthermore, the model maintains high interpretability, providing insights into the intricate mechanisms of offensive content propagation. Our findings underscore the potential of HDLA in fostering healthier online communities by efficiently curating digital content, thereby upholding the integrity of digital communication spaces.

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“…A single ML algorithm may not be sufficient to accurately detect all instances of cyberbullying. By combining the predictions of multiple models, ensemble learning can leverage the strengths of different algorithms and overcome the limitations of a single model [21,22]. For example, some algorithms may be better at detecting certain types of cyberbullying, while others may perform better on different types.…”

Section: Related Workmentioning

confidence: 99%

Cyberbullying Detection on Social Media Using Stacking Ensemble Learning and Enhanced BERT

Muneer,

Alwadain,

Ragab

et al. 2023

Information

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The prevalence of cyberbullying on Social Media (SM) platforms has become a significant concern for individuals, organizations, and society as a whole. The early detection and intervention of cyberbullying on social media are critical to mitigating its harmful effects. In recent years, ensemble learning has shown promising results for detecting cyberbullying on social media. This paper presents an ensemble stacking learning approach for detecting cyberbullying on Twitter using a combination of Deep Neural Network methods (DNNs). It also introduces BERT-M, a modified BERT model. The dataset used in this study was collected from Twitter and preprocessed to remove irrelevant information. The feature extraction process involved utilizing word2vec with Continuous Bag of Words (CBOW) to form the weights in the embedding layer. These features were then fed into a convolutional and pooling mechanism, effectively reducing their dimensionality, and capturing the position-invariant characteristics of the offensive words. The validation of the proposed stacked model and BERT-M was performed using well-known model evaluation measures. The stacked model achieved an F1-score of 0.964, precision of 0.950, recall of 0.92 and the detection time reported was 3 min, which surpasses the previously reported accuracy and speed scores for all known NLP detectors of cyberbullying, including standard BERT and BERT-M. The results of the experiment showed that the stacking ensemble learning approach achieved an accuracy of 97.4% in detecting cyberbullying on Twitter dataset and 90.97% on combined Twitter and Facebook dataset. The results demonstrate the effectiveness of the proposed stacking ensemble learning approach in detecting cyberbullying on SM and highlight the importance of combining multiple models for improved performance.

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COVID-19 and cyberbullying: deep ensemble model to identify cyberbullying from code-switched languages during the pandemic

Cited by 12 publications

References 16 publications

Cyberbullying Detection and Severity Determination Model

Cyberbullying Detection and Severity Determination Model

Offensive Language Detection on Online Social Networks using Hybrid Deep Learning Architecture

Cyberbullying Detection on Social Media Using Stacking Ensemble Learning and Enhanced BERT

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