Arabic Cyberbullying Detection: Using Deep Learning

Cyberbullying is a growing and significant problem in today’s workplace. Existing automated cyberbullying detection solutions rely on machine learning and deep learning techniques. It is proven that the deep learning-based approaches produce better accuracy for text-based classification than other existing approaches. A novel decentralized deep learning approach called MaLang is developed to detect abusive textual content. MaLang is deployed at two levels in a network: (1) the System Level and (2) the Cloud Level, to tackle the usage of toxic or abusive content on any messaging application within a company’s networks. The system-level module consists of a simple deep learning model called CASE that reads the user’s messaging data and classifies them into abusive and non-abusive categories, without sending any raw or readable data to the cloud. Identified abusive messages are sent to the cloud module with a unique identifier to keep user profiles hidden. The cloud module, called KIPP, utilizes deep learning to determine the probability of a message containing different categories of toxic content, such as: ‘Toxic’, ‘Insult’, ‘Threat’, or ‘Hate Speech’. MaLang achieves a 98.2% classification accuracy that outperforms other current cyberbullying detection systems.

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Traditional Classifiers vs. Deep Learning for Cyberbullying Detection

Advances in Human and Social Aspects of Technology - Automatic Cyberbullying Detection ◽

10.4018/978-1-5225-5249-9.ch006 ◽

2018 ◽

pp. 133-149

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Search Algorithm ◽

Learning Approach ◽

Brute Force ◽

Brute Force Search ◽

Novel Method ◽

Objective View ◽

Cyberbullying Detection

In this chapter, the authors present their approach to cyberbullying detection with the use of various traditional classifiers, including a deep learning approach. Research has tackled the problem of cyberbullying detection during recent years. However, due to complexity of language used in cyberbullying, the results obtained with traditional classifiers has remained only mildly satisfying. In this chapter, the authors apply a number of traditional classifiers, used also in previous research, to obtain an objective view on to what extent each of them is suitable to the task. They also propose a novel method to automatic cyberbullying detection based on convolutional neural networks and increased feature density. The experiments performed on actual cyberbullying data showed a major advantage of the presented approach to all previous methods, including the two best performing methods so far based on SO-PMI-IR and brute-force search algorithm, presented in previous two chapters.

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Cyberbullying Detection for Online Games Chat Logs using Deep Learning

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) ◽

10.1109/hnicem48295.2019.9072811 ◽

2019 ◽

Author(s):

James Albert Cornel ◽

Carl Christian Pablo ◽

Jan Arnold Marzan ◽

Vince Julius Mercado ◽

Bernie Fabito ◽

...

Keyword(s):

Deep Learning ◽

Online Games ◽

Cyberbullying Detection

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Multi-featured Cyberbullying Detection Based on Deep Learning

10.1109/iccse51940.2021.9569270 ◽

2021 ◽

Author(s):

Yue Luo ◽

Xurui Zhang ◽

Jiahao Hua ◽

Weixuan Shen

Keyword(s):

Deep Learning ◽

Cyberbullying Detection

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A STUDY ON DEEP LEARNING ALGORITHMS FOR MULTIMODAL AND MULTILINGUAL CYBERBULLYING DETECTION

10.36106/ijar/4620274 ◽

2021 ◽

pp. 50-54

Author(s):

Vijayakumar V ◽

Hari Prasad D

Keyword(s):

Deep Learning ◽

Text Messages ◽

Cyber Bullying ◽

Physical And Mental Health ◽

Modal Data ◽

Learning Techniques ◽

The Social ◽

Social Media Platforms ◽

Cyberbullying Detection ◽

Multiple Languages

With the increased utilization of the internet and social media platforms, can foster destructive or harmful behaviors such as cyberbullying. Cyberbullying poses signicant threat to physical and mental health of the victims. There is a demand for automatic detection and prevention of cyberbullying. In Social networks, there is a big challenge to detect the cyber bullying event and to control all the cyberbullying content and languages that users post. Due to complexity of multiple languages and cross-mix languages used in cyberbullying, the detection has remained only mildly satisfying. And also recently, images and videos dominate the social feeds in addition to text messages and comments. Machine learning and deep learning techniques can be helpful to detect the bullies and can generate a model to automatically detect multi-lingual cyberbullying actions. Deep neural architectures are useful to model, learn and fuse multi-modal data for cyber bullying detection. This paper proposes a detailed review on machine and deep learning approach for detecting and preventing multimodal and multilingual cyberbullying.

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