Social Media Cyberbullying Detection using Machine Learning

Cyberbullying detection is a rising research topic due to its paramount impact on social media users, especially youngsters and adolescents. While there has been an enormous amount of progress in utilising efficient machine learning and NLP techniques for tackling this task, recent methods have not fully addressed contextualizing the textual content to the highest possible extent. The textual content of social media posts and comments is normally long, noisy and mixed with lots of irrelevant tokens and characters, and therefore utilizing an attention-based approach that can focus on more relevant parts of the text can be quite pertinent. Moreover, social media information is normally multi-modal in nature and may contain various metadata and contextual information that can contribute to enhancing the Cyberbullying prediction system. In this research, we propose a novel machine learning method that, (i) fine tunes a variant of BERT, a deep attention-based language model, which is capable of detecting patterns in long and noisy bodies of text; (ii)~extracts contextual information from multiple sources including metadata information, images and even external knowledge sources and uses these features to complement the learner model; and (iii) efficiently combines textual and contextual features using boosting and a wide-and-deep architecture. We compare our proposed method with state-of-the-art methods and highlight how our approach significantly outperforming the quality of results compared to those methods in most cases.

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A Systematic Review of Machine Learning Algorithms in Cyberbullying Detection: Future Directions and Challenges

Journal of Information Security and Cybercrimes Research ◽

10.26735/gbtv9013 ◽

2021 ◽

Vol 4 (1) ◽

pp. 01-26

Author(s):

Muhammad Arif

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Social Media ◽

Language Processing ◽

Machine Learning Algorithms ◽

Future Directions ◽

Social Media Networks ◽

Current State ◽

Art Research ◽

Cyberbullying Detection

Social media networks are becoming an essential part of life for most of the world’s population. Detecting cyberbullying using machine learning and natural language processing algorithms is getting the attention of researchers. There is a growing need for automatic detection and mitigation of cyberbullying events on social media. In this study, research directions and the theoretical foundation in this area are investigated. A systematic review of the current state-of-the-art research in this area is conducted. A framework considering all possible actors in the cyberbullying event must be designed, including various aspects of cyberbullying and its effect on the participating actors. Furthermore, future directions and challenges are also discussed.

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Blockchain Leveraged Cyberbullying Preventing framework

10.21203/rs.3.rs-21075/v2 ◽

2021 ◽

Author(s):

Md Anawar Hossen Wadud ◽

Md Ashraf Uddin

Keyword(s):

Machine Learning ◽

Social Media ◽

Language Processing ◽

Processing Technique ◽

The Internet ◽

Machine Learning Method ◽

Learning Method ◽

Natural Language Processing Technique ◽

Being Bullied ◽

Cyberbullying Detection

Abstract The popularity of social media has exploded worldwide over the last few decades and becomes the most preferred mode of social interaction. The internet also provides a new platform through which adolescents are being bullied. Appropriate means of cyberbullying detection is still partial and in some cases very limited. Moreover, research on cyberbullying detection extensively focuses on surveys and its psychological impacts on victims. However, prevention has not been widely addressed. To bridge the gap, this paper aims to detect cyberbullying efficiently. This paper employs a standard machine learning method and natural language processing technique as a part of the detection process in decentralized Blockchain leveraged architecture. We provide a fog based architecture for cyberbullying detection, aiming at relieving the server's load by placing the detection and the prevention of cyberbullying processes at the fog layer. The proposal might offer a probable solution to save users, particularly adolescents from severe consequences of cyberbullying.

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Blockchain Leveraged Cyberbullying Preventing framework

10.21203/rs.3.rs-21075/v1 ◽

2020 ◽

Author(s):

Md Anawar Hossen Wadud ◽

Md Ashraf Uddin ◽

Shamima Parvez ◽

Mohammad Motiur Rahman ◽

Ammar Alazab ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Language Processing ◽

Processing Technique ◽

The Internet ◽

Machine Learning Method ◽

Learning Method ◽

Natural Language Processing Technique ◽

Being Bullied ◽

Cyberbullying Detection

Abstract The popularity of social media has exploded worldwide over the last few decades and becomes the most preferred mode of social interaction. The internet also provides a new platform through which adolescents are being bullied. Appropriate means of cyberbullying detection is still partial and in some cases very limited. Moreover, research on cyberbullying detection extensively focuses on surveys and its psychological impacts on victims. However, prevention has not been widely addressed. To bridge the gap, this paper aims to detect cyberbullying efficiently. This paper employs a standard machine learning method and natural language processing technique as a part of the detection process in decentralized Blockchain leveraged architecture. We provide a fog based architecture for cyberbullying detection, aiming at relieving the server's load by placing the detection and the prevention of cyberbullying processes at the fog layer. The proposal might offer a probable solution to save users, particularly adolescents from severe consequences of cyberbullying.

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Predicting and Detecting Hectoring on Social Media Using Machine Learning

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v5i8.173176 ◽

2017 ◽

Vol 5 (8) ◽

pp. 173-176 ◽

Cited By ~ 1

Author(s):

Sakshi Gujral

Keyword(s):

Machine Learning ◽

Social Media

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Utilizing Blockchain Technology in Social Media Bot Identification

10.36227/techrxiv.12049374 ◽

2020 ◽

Author(s):

Shreya Reddy ◽

Lisa Ewen ◽

Pankti Patel ◽

Prerak Patel ◽

Ankit Kundal ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Gold Standard ◽

The Internet ◽

Learning Models ◽

Current Time ◽

Machine Learning Methods ◽

Blockchain Technology ◽

Modern Age ◽

Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201844 ◽

2021 ◽

Vol 40 (5) ◽

pp. 9361-9382 ◽

Cited By ~ 1

Author(s):

Naeem Iqbal ◽

Rashid Ahmad ◽

Faisal Jamil ◽

Do-Hyeun Kim

Keyword(s):

Machine Learning ◽

Social Media ◽

Resource Planning ◽

Experimental Results ◽

Quality Prediction ◽

Classification Models ◽

Hybrid Features ◽

Social Media Data ◽

Media Data

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.

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