Cyberbullying Detection on Social Networks Using Machine Learning Approaches

Islam, Manowarul; Uddin, Md. Ashraf; Islam, Linta; Akter, Arnisha; Sharmin, Selina; Acharjee, Uzzal Kumar

doi:10.1109/csde50874.2020.9411601

Cited by 61 publications

(17 citation statements)

References 20 publications

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“…The current social networks are regularly misused to post or spread CSA messages, comments and chat conversation. Hence, the 24 research papers [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [24], [25], [27], [28], [29], [30], [31], [32], [37], [38], [40], [41] and [43] proposed methods to detect, prevent and/or process CSAMs in text format.…”

Section: ) Working On Textmentioning

confidence: 99%

“…To provide a safe environment for children in online networks, computer scientists applied artificial intelligent algorithms for: (1) Detecting abusive comments, chat conversations and/or messages ( [9], [10], [38], [31], [41]); (2) Analysing responses and posts on Twitter and Facebook in India ( [27]);…”

Section: ) Working On Textmentioning

confidence: 99%

“…1) Self-Build: Twenty-one research works [10], [11], [12], [14], [15], [16], [20], [21], [23], [24], [27], [31], [32], [33], [34], [37], [39], [40], [41], [42] and [43] created their own datasets to evaluate and discuss their systems. For example, Quayle [17] collected CSA text from social networks and media sources, namely Facebook, BBC news, Skype, WhatsApp and Instagram.…”

Section: B Datasets Usedmentioning

confidence: 99%

See 2 more Smart Citations

Investigation, Detection and Prevention of Online Child Sexual Abuse Materials: A Comprehensive Survey

Ngo¹,

Dang²,

Thorpe³

et al. 2023

Preprint

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Child sexual abuse inflicts lifelong devastating consequences for victims and is an ongoing social concern. In most countries, child sexual abuse material (CSAM) distribution is illegal. As a result, there are many research papers in the literature which proposed technologies to detect and investigate CSAM. In this survey, a comprehensive search of the peer-reviewed journal and conference paper databases (including preprints) is conducted to identify high-quality literature. We use the PRISMA methodology to refine our search space to 2,761 papers published by Springer, Elsevier, IEEE and ACM. After iterative reviews of title, abstract and full text for relevance to our topics, 43 papers are included for full review. Our paper provides a comprehensive synthesis of the tasks of the current research and how the papers use techniques and datasets to solve their tasks and evaluate their models. To the best of our knowledge, we are the first to focus exclusively on online CSAM detection and prevention with no geographic boundaries, and the first survey to review papers published after 2018. It can be used by researchers to identify gaps in knowledge and relevant publicly available datasets that may be useful for their research.

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Section: ) Working On Textmentioning

confidence: 99%

Section: ) Working On Textmentioning

confidence: 99%

Section: B Datasets Usedmentioning

confidence: 99%

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Investigation, Detection and Prevention of Online Child Sexual Abuse Materials: A Comprehensive Survey

Ngo¹,

Dang²,

Thorpe³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Social networking sites, such as Facebook, Twitter, etc., become inextricably linked to the lives of their users. As a result, these platforms are the most common forms of cyberbullying victimization, and their admiration and widespread use have increased the number of cybercrime incidents [6]. This rise is generally attributed to traditional bullying being more challenging to perpetrate than cyberbullying, in that bullies bully their victims without being confronted directly through the associated author.…”

Section: Introductionmentioning

confidence: 99%

Detecting Cybercrime: An Evaluation of Machine Learning and Deep Learning Using Natural Language Processing Techniques on the Social Network

Amer

Siddiqui

Athamena

2022

Preprint

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The widespread use of online social networks has culminated in across-the-board social communication among users, resulting in a considerable amount of user-generated contact data. Cybercrime has become a significant issue in recent years with the rise of online communication and social network. Cybercrime has lately been identified as a severe national psychological concern among platform users, and building a reliable detection model is crucial. Cyberbullying is the phrase used to describe such online harassment, insults, and attacks. It has become challenging to identify such unauthorized content due to the massive number of user-generated content. Because deep neural networks have various advantages over conventional machine learning approaches, researchers are turning to them more frequently to identify cyberbullying. Deep learning and machine learning have several uses in text classification. This article suggested the novel neural network model through parameters of an algorithmic and optimization comparative analysis of nine category approaches, four neural networks, and five machine learning, in two scenarios with real-world datasets of cyberbullying. Moreover, this work also analyzes the impact of word embedding and feature extraction techniques based on text mining and NLP on algorithms' performances. We performed extensive experiments on the two scenarios with a split dataset to demonstrate the merit of this research, comparing nine classification approaches through five feature extraction techniques. Our proposed cybercriminal detection model using neural networks, deep learning, and machine learning outperforms the existing state-of-the-art method of cybercriminal detection in terms of accuracy achieving higher performance.

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“…Taking into consideration the dangers mentioned above, it is decisive to find a way to detect accurately any type of cyberbullying in an automatic and quickly way. The majority of the proposed solutions in special literature about cyberbullying detection are based on supervised approaches [6,9]. As a general conclusion of these kind of research papers is that their big limitations are related to the insufficient amount of labeled data that implies, in most of the cases, the overfitting of the models.…”

mentioning

confidence: 99%

Enhancing Cyberbullying Detection in Social Media using Semi-supervised Learning

Ionescu¹,

Dumitrescu²,

Rebedea³

2022

RoCHI - International Conference on Human-Computer Interaction

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Cyberbullying has become a usual form of harassment nowadays because most of the time we use digital technologies to communicate with others. This type of bullying can affect our mental, emotional, and also physical health 1 . Also, the significant impact of cyberbullying is that it can spread easily and quickly around the world. In most of the cases, a cyberbullying attack is discovered too late, after all the negative effects have already affected the assaulted person. The researchers tried to find an automatic way to discover a potential cyberbullying attack on social media using the power of machine learning 2 The majority of the proposed solutions are classic supervised approaches as Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, though it was tried also semi-supervised or unsupervised approaches. This paper's aim is to leverage the large amount of unlabeled data that can easily be collected and using them alongside with semi-supervised learning approaches in order to solve the task of cyberbullying detection.

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Cyberbullying Detection on Social Networks Using Machine Learning Approaches

Cited by 61 publications

References 20 publications

Investigation, Detection and Prevention of Online Child Sexual Abuse Materials: A Comprehensive Survey

Investigation, Detection and Prevention of Online Child Sexual Abuse Materials: A Comprehensive Survey

Detecting Cybercrime: An Evaluation of Machine Learning and Deep Learning Using Natural Language Processing Techniques on the Social Network

Enhancing Cyberbullying Detection in Social Media using Semi-supervised Learning

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