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

Arif, Muhammad

doi:10.26735/gbtv9013

Cited by 29 publications

(9 citation statements)

References 128 publications

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“…Arif in [ 25 ] and Singh et al in [ 26 ] recently provided extensive reviews of relevant research works using machine learning techniques to detect cyberbullying on social media. From a generic perspective, most works use content-based features (such as Tf-idf or Word2Vec) and sentiment analysis as a basis, while others also incorporate different features extracted from the user, such as social features (e.g., followers) or their profile (e.g., age or gender).…”

Section: Related Workmentioning

confidence: 99%

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

López-Vizcaíno¹,

Nóvoa²,

Artières

et al. 2023

Sensors

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The rise in the use of social media networks has increased the prevalence of cyberbullying, and time is paramount to reduce the negative effects that derive from those behaviours on any social media platform. This paper aims to study the early detection problem from a general perspective by carrying out experiments over two independent datasets (Instagram and Vine), exclusively using users’ comments. We used textual information from comments over baseline early detection models (fixed, threshold, and dual models) to apply three different methods of improving early detection. First, we evaluated the performance of Doc2Vec features. Finally, we also presented multiple instance learning (MIL) on early detection models and we assessed its performance. We applied timeawareprecision (TaP) as an early detection metric to asses the performance of the presented methods. We conclude that the inclusion of Doc2Vec features improves the performance of baseline early detection models by up to 79.6%. Moreover, multiple instance learning shows an important positive effect for the Vine dataset, where smaller post sizes and less use of the English language are present, with a further improvement of up to 13%, but no significant enhancement is shown for the Instagram dataset.

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Section: Related Workmentioning

confidence: 99%

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

López-Vizcaíno¹,

Nóvoa²,

Artières

et al. 2023

Sensors

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“…The escalated usage of social networking sites and freedom of speech has given optimal ground to individuals across all demographics for cyberbullying and cyberaggression. This leaves drastic and noticeable impacts on behavior of a victim, ranging from disturbance in emotional wellbeing and isolation from society to more severe and deadly consequences [ 29 ]. Automatic Cyberbullying detection has remained very challenging task since social media content is in natural language and is usually posted in unstructured free-text form leaving behind the language norms, rules, and standards.…”

Section: Problem Statementmentioning

confidence: 99%

Cyberbullying detection: advanced preprocessing techniques & deep learning architecture for Roman Urdu data

2021

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Social media have become a very viable medium for communication, collaboration, exchange of information, knowledge, and ideas. However, due to anonymity preservation, the incidents of hate speech and cyberbullying have been diversified across the globe. This intimidating problem has recently sought the attention of researchers and scholars worldwide and studies have been undertaken to formulate solution strategies for automatic detection of cyberaggression and hate speech, varying from machine learning models with vast features to more complex deep neural network models and different SN platforms. However, the existing research is directed towards mature languages and highlights a huge gap in newly embraced resource poor languages. One such language that has been recently adopted worldwide and more specifically by south Asian countries for communication on social media is Roman Urdu i-e Urdu language written using Roman scripting. To address this research gap, we have performed extensive preprocessing on Roman Urdu microtext. This typically involves formation of Roman Urdu slang- phrase dictionary and mapping slangs after tokenization. We have also eliminated cyberbullying domain specific stop words for dimensionality reduction of corpus. The unstructured data were further processed to handle encoded text formats and metadata/non-linguistic features. Furthermore, we performed extensive experiments by implementing RNN-LSTM, RNN-BiLSTM and CNN models varying epochs executions, model layers and tuning hyperparameters to analyze and uncover cyberbullying textual patterns in Roman Urdu. The efficiency and performance of models were evaluated using different metrics to present the comparative analysis. Results highlight that RNN-LSTM and RNN-BiLSTM performed best and achieved validation accuracy of 85.5 and 85% whereas F1 score was 0.7 and 0.67 respectively over aggression class.

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“…Due to this peculiarity, it is essential to address the effect cyberaggression has on different ages, basically juvenile ages yet in addition arising grown-ups have given some knowledge into how they adapt or oversee cyberbullying (CB). It can take different structures, for example, sending undesirable, disparaging, or compromising remarks, spreading bits of hearsay, sending pictures or recordings that are hostile or humiliating by message, email, talk, or posting on sites including OSN [4,5]. The different kinds of CB in OSN are shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Mengash¹,

Eltahir²,

Al‐Wesabi³

et al. 2023

Computer Systems Science and Engineering

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Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, ehealthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN model. The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites. The SRO-MLCOSN model initially employs Glove technique for word embedding process. Besides, a multiclass-weighted kernel extreme learning machine (M-WKELM) model is utilized for effectual identification and categorization of CB. Finally, Search and Rescue Optimization (SRO) algorithm is exploited to fine tune the parameters involved in the M-WKELM model. The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision, recall, and F1-score of 96.24%, 98.71%, and 97.46% respectively.

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

Cited by 29 publications

References 128 publications

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks

Cyberbullying detection: advanced preprocessing techniques & deep learning architecture for Roman Urdu data

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

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