A Mobile-Based System for Preventing Online Abuse and Cyberbullying

Salawu, Semiu; Lumsden, Jo; He, Yulan

doi:10.1007/s42380-021-00115-5

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…Our results reveal that deep learning techniques, particularly BiLSTM, outperform the shallow learning methods in detecting hate speech on Twitter [11]. BiLSTM demonstrates a superior ability to capture the intricate nature of hate speech by understanding context, semantic nuances, and sequential patterns in tweets [12]. This finding underscores the potential of deep learning techniques in addressing the challenge of hate speech detection and monitoring on social media platforms.…”

Section: Introductionmentioning

confidence: 69%

Hate Speech Detection in Social Networks using Machine Learning and Deep Learning Methods

Toktarova,

Syrlybay,

Myrzakhmetova

et al. 2023

IJACSA

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Hate speech on social media platforms like Twitter is a growing concern that poses challenges to maintaining a healthy online environment and fostering constructive communication. Effective detection and monitoring of hate speech are crucial for mitigating its adverse impact on individuals and communities. In this paper, we propose a comprehensive approach for hate speech detection on Twitter using both traditional machine learning and deep learning techniques. Our research encompasses a thorough comparison of these techniques to determine their effectiveness in identifying hate speech on Twitter. We construct a robust dataset, gathered from diverse sources and annotated by experts, to ensure the reliability of our models. The dataset consists of tweets labeled as hate speech, offensive language, or neutral, providing a more nuanced representation of online discourse. We evaluate the performance of LSTM, BiLSTM, and CNN models against traditional shallow learning methods to establish a baseline for comparison. Our findings reveal that deep learning techniques outperform shallow learning methods, with BiLSTM emerging as the most accurate model for hate speech detection. The BiLSTM model demonstrates improved sensitivity to context, semantic nuances, and sequential patterns in tweets, making it adept at capturing the intricate nature of hate speech. Furthermore, we explore the integration of word embeddings, such as Word2Vec and GloVe, to enhance the performance of our models. The incorporation of these embeddings significantly improves the models' ability to discern between hate speech and other forms of online communication. This paper presents a comprehensive analysis of various machine learning methods for hate speech detection on Twitter, ultimately demonstrating the superiority of deep learning techniques, particularly BiLSTM, in addressing this critical issue. Our findings pave the way for further research into advanced methods of tackling hate speech and facilitating healthier online interactions.

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Section: Introductionmentioning

confidence: 69%

Hate Speech Detection in Social Networks using Machine Learning and Deep Learning Methods

Toktarova,

Syrlybay,

Myrzakhmetova

et al. 2023

IJACSA

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“…The issue of early detection of cyberbullying within the realm of social networking platforms may inherently differ from the challenge associated with classifying distinct manifestations of cyberbullying [12]. In the context delineated herein, we identify a cohort of social media interactions collectively denoted as "S." Consequently, it becomes plausible that a subset of these interactions may indeed represent instances of cyberbullying.…”

Section: Problem Statementmentioning

confidence: 99%

Offensive Language Detection on Social Media using Machine Learning

Abdrakhmanov,

Kenesbayev,

Berkimbayev

et al. 2024

ijacsa

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This research paper addresses the critical issue of cyberbullying detection within the realm of social networks, employing a comprehensive examination of various machine learning and deep learning techniques. The study investigates the performance of these methodologies through rigorous evaluation using standard metrics, including Accuracy, Precision, Recall, Fmeasure, and AUC-ROC. The findings highlight the notable efficacy of deep learning models, particularly the Bidirectional Long Short-Term Memory (BiLSTM) architecture, in consistently outperforming alternative methods across diverse classification tasks. Confusion matrices and graphical representations further elucidate model performance, emphasizing the BiLSTM-based model's remarkable capacity to discern and classify cyberbullying instances accurately. These results underscore the significance of advanced neural network structures in capturing the complexities of online hate speech and offensive content. This research contributes valuable insights toward fostering safer and more inclusive online communities by facilitating early identification and mitigation of cyberbullying. Future investigations may explore hybrid approaches, additional feature integration, or realtime detection systems to further refine and advance the state-ofthe-art in addressing this critical societal concern.

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“…To assist online harassment, Gomez et al [11] applied traditional machine learning methods for text classification. Many authors have tried to identify cyberbullying using various machine learning algorithms all throughout the internet, particularly in social media [12]. The use of expanded machine learning approaches to identify cyberbullying has achieved significant progress in recent years [13].…”

Section: Related Workmentioning

confidence: 99%

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Sultan¹,

Toktarova²,

Zhumadillayeva³

et al. 2023

Computers, Materials &Amp; Continua

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Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology. The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location. These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior. However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions. Due to widespread use of the social networking platforms and technological gadgets, conventional bullying has migrated from physical form to online, where it is termed as Cyberbullying. However, recently the digital technologies as machine learning and deep learning have been showing their efficiency in identifying linguistic patterns used by cyberbullies and cyberbullying detection problem. In this research paper, we aimed to evaluate shallow machine learning and deep learning methods in cyberbullying detection problem. We deployed three deep and six shallow learning algorithms for cyberbullying detection problems. The results show that bidirectional long-short-term memory is the most efficient method for cyberbullying detection, in terms of accuracy and recall.

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A Mobile-Based System for Preventing Online Abuse and Cyberbullying

Cited by 5 publications

References 34 publications

Hate Speech Detection in Social Networks using Machine Learning and Deep Learning Methods

Hate Speech Detection in Social Networks using Machine Learning and Deep Learning Methods

Offensive Language Detection on Social Media using Machine Learning

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

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