From Machine Learning to Deep Learning for Detecting Abusive Messages in Arabic Social Media: Survey and Challenges

Azzi, Salma; Zribi, Chiraz Ben Othmane

doi:10.1007/978-3-030-71187-0_38

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…In this segment, we offer a juxtaposition of diverse machine learning paradigms geared towards the categorization of religious cyberbullying, leveraging assorted feature amalgamations. Our investigation encompasses a gamut of prevalent techniques for classifier formulation and tutelage [33][34][35]. Throughout the model training phase, we employed a spectrum of features, conducting myriad experiments with distinct feature sets.…”

Section: A Feature Engineeringmentioning

confidence: 99%

Hybrid Convolutional Recurrent Neural Network for Cyberbullying Detection on Textual Data

Baiganova,

Toxanova,

Yerekesheva

et al. 2024

ijacsa

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With the burgeoning use of social media platforms, online harassment and cyberbullying have become significant concerns. Traditional mechanisms often falter, necessitating advanced methodologies for efficient detection. This study presents an innovative approach to identifying cyberbullying incidents on social media sites, employing a hybrid neural network architecture that amalgamates Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). By harnessing the sequential processing capabilities of LSTM to analyze the temporal progression of textual data, and the spatial discernment of CNN to pinpoint bullying keywords and patterns, the model demonstrates substantial improvement in detection accuracy compared to extant methods. A diverse dataset, encompassing multiple social media platforms and linguistic styles, was utilized to train and test the model, ensuring robustness. Results evince that the LSTM-CNN amalgamation can adeptly handle varied sentence structures and contextual nuances, outstripping traditional machine learning classifiers in both specificity and sensitivity. This research underscores the potential of hybrid neural networks in addressing contemporary digital challenges, urging further exploration into blended architectures for nuanced problem-solving in cyber realms.

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Section: A Feature Engineeringmentioning

confidence: 99%

Hybrid Convolutional Recurrent Neural Network for Cyberbullying Detection on Textual Data

Baiganova,

Toxanova,

Yerekesheva

et al. 2024

ijacsa

View full text Add to dashboard Cite

show abstract

“…Several studies have focused on developing effective models for offensive language identification, primarily in well-resourced languages such as English, Spanish, and French [10]. However, the challenges associated with offensive language identification in low resource languages remain relatively unexplored [11]. In this literature review, we discuss the existing research and methodologies employed in offensive language identification, with a specific focus on low resource languages.…”

Section: Literature Reviewmentioning

confidence: 99%

Offensive Language Identification in Low Resource Languages using Bidirectional Long-Short-Term Memory Network

Toktarova,

Abushakhma,

Adylbekova

et al. 2023

IJACSA

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Offensive language identification is a critical task in today's digital era, enabling the development of effective content moderation systems. However, it poses unique challenges in low resource languages where limited annotated data is available. This research paper focuses on addressing the problem of offensive language identification specifically in the context of a low resource language, namely the Kazakh language. To tackle this challenge, we propose a novel approach based on Bidirectional Long-Short-Term Memory (BiLSTM) networks, which have demonstrated strong performance in natural language processing tasks. By leveraging the bidirectional nature of the BiLSTM architecture, we capture both contextual dependencies and long-term dependencies in the input text, enabling more accurate offensive language identification. Our approach further utilizes transfer learning techniques to mitigate the scarcity of annotated data in the low resource setting. Through extensive experiments on a Kazakh offensive language dataset, we demonstrate the effectiveness of our proposed approach, achieving state-of-the-art results in offensive language identification in the low resource Kazakh language. Moreover, we analyze the impact of different model configurations and training strategies on the performance of our approach. The findings from our study provide valuable insights into offensive language identification techniques in low resource languages and pave the way for more robust content moderation systems tailored to specific linguistic contexts.

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“…Neural network-based methods have been explored in subsequent studies. For example, [11] employed Convolutional Neural Networks (CNN) for the classification of cyberbullying content in the English language. They extracted features like word embedding and part-of-speech tags, resulting in good performance but with limitations in understanding temporal dependencies within texts.…”

Section: Related Workmentioning

confidence: 99%

Hybrid CNN-LSTM Network for Cyberbullying Detection on Social Networks using Textual Contents

Sultan,

Mendes,

Kassenkhan

et al. 2023

IJACSA

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In the face of escalating cyberbullying and its associated online activities, devising effective mechanisms for its detection remains a critical challenge. This study proposes an innovative approach, integrating Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN), for the detection of cyberbullying in online textual content. The method uses LSTM to understand the temporal aspects and sequential dependencies of text, while CNN is employed to automatically and adaptively learn spatial hierarchies of features. We introduce a hybrid LSTM-CNN model which has been designed to optimize the detection of potential cyberbullying signals within large quantities of online text, through the application of advanced natural language processing (NLP) techniques. The paper reports the results from rigorous testing of this model across an extensive dataset drawn from multiple online platforms, indicative of the current digital landscape. Comparisons were made with prevailing methods for cyberbullying detection, demonstrating a substantial improvement in accuracy, precision, recall and F1-score. This research constitutes a significant step forward in developing robust tools for detecting online cyberbullying, thereby enabling proactive interventions and informed policy development. The effectiveness of the LSTM-CNN hybrid model underscores the transformative potential of leveraging artificial intelligence for social safety and cohesion in an increasingly digitized society. The potential applications and limitations of this model, alongside avenues for future research, are discussed.

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From Machine Learning to Deep Learning for Detecting Abusive Messages in Arabic Social Media: Survey and Challenges

Cited by 7 publications

References 21 publications

Hybrid Convolutional Recurrent Neural Network for Cyberbullying Detection on Textual Data

Hybrid Convolutional Recurrent Neural Network for Cyberbullying Detection on Textual Data

Offensive Language Identification in Low Resource Languages using Bidirectional Long-Short-Term Memory Network

Hybrid CNN-LSTM Network for Cyberbullying Detection on Social Networks using Textual Contents

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