An Application to Detect Cyberbullying Using Machine Learning and Deep Learning Techniques

Raj, Mitushi; Singh, Samridhi; Solanki, Kanishka; Selvanambi, Ramani

doi:10.1007/s42979-022-01308-5

Cited by 42 publications

(12 citation statements)

References 25 publications

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“…Although this approach has shown high-level accuracy of cyberbullying detection, the authors were inspired toward better optimization by exploring deep learning models accessing their reliability on real-world high data. Furthermore, using deep neural networks (DNNs, Raj et al, 2022 ) proposed a model to detect cyberbullying in tweets and other social media posts. DNNs are effective compared to conventional techniques.…”

Section: Related Workmentioning

confidence: 99%

ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media

Nitya Harshitha,

Prabu,

Suganya

et al. 2024

Front. Artif. Intell.

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The emergence of social media has given rise to a variety of networking and communication opportunities, as well as the well-known issue of cyberbullying, which is continuously on the rise in the current world. Researchers have been actively addressing cyberbullying for a long time by applying machine learning and deep learning techniques. However, although these algorithms have performed well on artificial datasets, they do not provide similar results when applied to real-time datasets with high levels of noise and imbalance. Consequently, finding generic algorithms that can work on dynamic data available across several platforms is critical. This study used a unique hybrid random forest-based CNN model for text classification, combining the strengths of both approaches. Real-time datasets from Twitter and Instagram were collected and annotated to demonstrate the effectiveness of the proposed technique. The performance of various ML and DL algorithms was compared, and the RF-based CNN model outperformed them in accuracy and execution speed. This is particularly important for timely detection of bullying episodes and providing assistance to victims. The model achieved an accuracy of 96% and delivered results 3.4 seconds faster than standard CNN models.

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Section: Related Workmentioning

confidence: 99%

ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media

Nitya Harshitha,

Prabu,

Suganya

et al. 2024

Front. Artif. Intell.

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“…Çevrim içi istismarın tespiti, evrensel küfürlü dil tespit modelleri geliştirme çabalarıyla araştırmanın odak noktası olmuştur (Wang, Lu, Han, Long, & Poon, 2020, s. 6367). Son olarak, siber zorbalığı tespit etmek için makine öğrenimi ve derin öğrenme tekniklerinin kullanılması önerilmiş ve bu yaygın sorunu ele almak için yenilikçi yaklaşımlara duyulan ihtiyaç vurgulanmıştır (Raj, Singh, & Solanki, 2022).…”

Section: Dijital Tacizunclassified

Dijital Şiddet ve Sosyal Medya Üzerine Bibliyometrik Bir Araştırma

KARAKUŞ

2024

TRT Akademi

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Bu araştırma, sosyal medyada yaşanan dijital şiddetin etkilerini detaylı bir şekilde anlamayı hedeflemektedir. Çalışmanın kapsamı, dijital şiddetin çeşitli biçimlerini tehdit, taciz ve siber zorbalık gibi unsurları içermekte ve bu şiddet türlerinin sosyal medya platformlarındaki görünümünü incelemeyi amaçlamaktadır. Çalışmanın önemi, dijital şiddetin artan bir sorun olmasıyla birlikte, sosyal medyanın bu şiddetin hem yayılmasında bir araç olarak kullanılmasının hem de bu şiddeti ele almak için bir platform olarak işlev görmesinin vurgusunu yapmaktadır. Bu bağlamda, çalışma dijital şiddetin sosyal medya üzerindeki etkilerini anlamak adına önemli bir adım olarak öne çıkmaktadır. Araştırmada, mevcut bilgileri derlemek ve analiz etmek amacıyla bibliyometrik bir yöntem kullanılmıştır. Bu şekilde, dijital şiddetin sosyal medya üzerindeki etkilerine dair daha derinlemesine anlayış elde edilmesine katkı sağlanacaktır. Çalışmada, "dijital şiddet" ve "sosyal medya" kavramlarına odaklanarak WEB of Science veri tabanında yer alan 20 çalışma incelenmiştir. Çalışmaların 2016 yılında başladığı ve en fazla çalışmanın iletişim alanında yapıldığı belirlenmiştir. Çalışmaların coğrafi dağılımında ise en fazla çalışmanın Kanada ve İngiltere'de olduğu gözlemlenmiştir. Türkiye'den herhangi bir çalışma bulunmamaktadır. Kurumsal açıdan incelendiğinde, en fazla çalışmanın York ve Coimbra Üniversiteleri'nde olduğu tespit edilmiştir. Ortak anahtar kelime analizi sonuçları, çalışmaların en az "sosyal medya platformları", "dijital kültür", "siber şiddet", "cinsiyet çalışmaları", "dijital teknolojiler" ve "toksik erkeklik" gibi konulara odaklandığını ortaya koymuştur.

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“…Among the models, the XLM-RoBERTa model had the best accuracy rate (85%) and F1 score (86%). Raj et al proposed a deep learning framework that will evaluate cyberbullying in real-time Twitter tweets [28]. Three datasets were collected from different resources containing English texts, Hindi texts, and the last one contains a combination of Hindi and English texts.…”

Section: Emon Et Al In 2022 Suggested a Model For Locatingmentioning

confidence: 99%

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

Haider Ameen,

Saad Al-Mukhtar,

Qahtan Ahmed

et al. 2024

ANJS

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Cyberbullying is a new form of online violence that has emerged as a result of the social media industry's explosive expansion since it allows for indirect communication. Despite the usefulness of digital media, it has been used by weak people to threaten and bully others online. In the last ten years, research has shown that children and teenagers are increasingly experiencing cyberbullying as a concern. This paper examines the research conducted during the previous ten years, categorizes it, and presents statistical analyses of the data collected during that time. A table is used to present various data, including the dataset that was used, its size (number of samples, posts, or messages), the methods that were employed, and the metrics that were gathered from the examined research that was taken from the publications that were looked into. This survey will be helpful to everyone who wants to advance their understanding of how machine learning may be used to identify cyberbullying, and it may help create a social media environment that is safe and relatively healthy by automatically identifying bullying communications.

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An Application to Detect Cyberbullying Using Machine Learning and Deep Learning Techniques

Cited by 42 publications

References 25 publications

ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media

ProTect: a hybrid deep learning model for proactive detection of cyberbullying on social media

Dijital Şiddet ve Sosyal Medya Üzerine Bibliyometrik Bir Araştırma

A Review of the Past Decade's Research Outcomes on Classifying Cyberbullying

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