Firefly-CDDL:A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community platforms.Among them is cyberbullying,which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react effectively.An alarmingly high percentage of people,especially teenagers,have reported being cyberbullied in recent years.A variety of approaches have been developed to detect cyberbullying,but they require time-consuming feature extraction and selection processes.Moreover,no approach to date has examined the meanings of words and the semantics involved in cyberbullying.In past work,we proposed an algorithm called Cyberbullying Detection Based on Deep Learning(CDDL)to bridge this gap.It eliminates the need for feature engineering and generates better predictions than traditional approaches for detecting cyberbullying.This was accomplished by incorporating deep learning—specifically,a convolutional neural network(CNN)—into the detection process.Although this algorithm shows remarkable improvement in performance over traditional detection mechanisms,one problem with it persists:CDDL requires that many parameters(filters,kernels,pool size,and number of neurons)be set prior to classification.These parameters play a major role in the quality of predictions,but a method for finding a suitable combination of their values remains elusive.To address this issue,we propose an algorithm called firefly-CDDL that incorporates a firefly optimisation algorithm into CDDL to automate the hitherto-manual trial-and-error hyperparameter setting.The proposed method does not require features for its predictions and its detection of cyberbullying is fully automated.The firefly-CDDL outperformed prevalent methods for detecting cyberbullying in experiments and recorded an accuracy of 98%within acceptable polynomial time.

The Relationship between Internet Addiction and Cyberbullying Perpetration: A Moderated Mediation Model of Moral Disengagement and Internet Literacy

Internet addiction and cyberbullying have emerged as significant global mental health concerns in recent years.Although previous studies have shown a close association between Internet addiction and cyberbullying,the underlying mechanisms connecting these two phenomena remain unclear.Therefore,this study aimed to reveal the mechanisms involved between Internet addiction and cyberbullying perpetration from the perspective of cognition function.This study recruited 976 Chinese youth through online survey,using the short version of Internet Addiction Test(s-IAT),Chinese Cyberbullying Intervention Project Questionnaire(C-CIPQ),Cyberbullying Moral Disengagement Scale(CMDS),and Internet Literacy Questionnaire(ILQ)to investigate the relationship between Internet addiction,moral disengagement,Internet literacy and cyberbullying perpetration.The keyfindings of this study were as follows:after controlling gender and age,(1)Internet addiction had a significant positive predictive effect on cyberbullying perpetration;(2)moral disengagement acted as a mediator between Internet addiction and cyberbullying perpetration;and(3)Internet literacy played a moderating role between moral disengagement and cyberbullying perpetration.In conclusion,there was a moderated mediating effect between Internet addiction and cyberbullying perpetration,contributing to a better understanding of the relationship between these two phenomena.

A Review of Machine Learning Techniques in Cyberbullying Detection

Automatic identification of cyberbullying is a problem that is gaining traction,especially in the Machine Learning areas.Not only is it complicated,but it has also become a pressing necessity,considering how social media has become an integral part of adolescents’lives and how serious the impacts of cyberbullying and online harassment can be,particularly among teenagers.This paper contains a systematic literature review of modern strategies,machine learning methods,and technical means for detecting cyberbullying and the aggressive command of an individual in the information space of the Internet.We undertake an in-depth review of 13 papers from four scientific databases.The article provides an overview of scientific literature to analyze the problem of cyberbullying detection from the point of view of machine learning and natural language processing.In this review,we consider a cyberbullying detection framework on social media platforms,which includes data collection,data processing,feature selection,feature extraction,and the application ofmachine learning to classify whether texts contain cyberbullying or not.This article seeks to guide future research on this topic toward a more consistent perspective with the phenomenon’s description and depiction,allowing future solutions to be more practical and effective.

Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology.The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location.These new platforms have ushered in a new age of user-generated content,online chats,social network and comprehensive data on individual behavior.However,the abuse of communication software such as social media websites,online communities,and chats has resulted in a new kind of online hostility and aggressive actions.Due to widespread use of the social networking platforms and technological gadgets,conventional bullying has migrated from physical form to online,where it is termed as Cyberbullying.However,recently the digital technologies as machine learning and deep learning have been showing their efficiency in identifying linguistic patterns used by cyberbullies and cyberbullying detection problem.In this research paper,we aimed to evaluate shallow machine learning and deep learning methods in cyberbullying detection problem.We deployed three deep and six shallow learning algorithms for cyberbullying detection problems.The results show that bidirectional long-short-term memory is the most efficient method for cyberbullying detection,in terms of accuracy and recall.

Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our lives.Cyberbullying is a social media network issue,a global crisis affecting the victims and society as a whole.It results from a misunderstanding regarding freedom of speech.In this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,Naïve Bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet allocation.In addition,we used accuracy,precision,recall,and F1 score to assess prior classifiers.Results show that the use of logistic regression,support vector machine,random forest model,and Naïve Bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.

A Critical Discourse Analysis of Cyberbullying Language Based on Text-mining Techniques——A Case Study of Prince Harry and Meghan Markle

With the development of AI,a large amount of cyberbullying language flooded into the Internet.Cyberbullying language damages the online environment and causes mental harm to the victims of online bullying.Many researchers,therefore,have paid much attention to the problem.This study collected online comments targeted at Prince Harry and Meghan Markle as a corpus and then analyzed text data based on Critical Discourse Analysis by using text-mining tools to explore the factors that contribute to the social ideological effects of the cyberbullying language.The research results show that cultural differences,prejudice,or social exclusion due to race or gender form cyberbullying on social media.

Social Media Cyberbullying Detection on Political Violence from Bangla Texts Using Machine Learning Algorithm

When someone threatens or humiliates another person online by sending those unpleasant messages or comments, this is known as Cyberbullying. Recently, Bangla text has been used much more often on social media. People communicate with others on social media through messages and comments. So bullies use social media as a rich environment to bully others, especially on political issues. Fights over Cyberbullying on political and social media posts are common today. Most of the time, it does a lot of damage. However, few works have been done for monitoring Bangla text on social media & no work has been done yet for detecting the bullying Bangla text on political issues due to the lack of annotated corpora and morphologic analyzers. In this work, we used several machine learning classifiers & a model. That will help to detect the Bangla bullying texts on social media. For this work, 11,000 Bangla texts have been collected from the comments section of political Facebook posts to make a new dataset and labelled the data as either bullied or not. This dataset has been used to train the machine learning classifier. The results indicate that Random Forest achieves superior accuracy of 91.08%.

Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

Cyberbullying(CB)is a challenging issue in social media and it becomes important to effectively identify the occurrence of CB.The recently developed deep learning(DL)models pave the way to design CB classifier models with maximum performance.At the same time,optimal hyperparameter tuning process plays a vital role to enhance overall results.This study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification(TLGODL-CBC)model in Social Media.The proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media context.Initially,the input data is cleaned and pre-processed to make it compatible for further processing.Followed by,independent recurrent autoencoder(IRAE)model is utilized for the recognition and classification of CBs.Finally,the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the work.To assuring the improved outcomes of the TLGODLCBC approach,a wide range of simulations are executed and the outcomes are investigated under several aspects.The simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches.

Cyberbullying and Cyberviolence Detection:A Triangular User-Activity-Content View

Recent years have witnessed the increasing popularity of mobile and networking devices,as well as social networking sites,where users engage in a variety of activities in the cyberspace on a daily and real-time basis.While such systems provide tremendous convenience and enjoyment for users,malicious usages,such as bullying,cruelty,extremism,and toxicity behaviors,also grow noticeably,and impose significant threats to individuals and communities.In this paper,we review computational approaches for cyberbullying and cyberviolence detection,in order to understand two major factors:1)What are the defining features of online bullying users,and 2)How to detect cyberbullying and cyberviolence.To achieve the goal,we propose a user-activities-content(UAC)triangular view,which defines that users in the cyberspace are centered around the UAC triangle to carry out activities and generate content.Accordingly,we categorize cyberbully features into three main categories:1)User centered features,2)Content centered features,and 3)Activity centered features.After that,we review methods for cyberbully detection,by taking supervised,unsupervised,transfer learning,and deep learning,etc.,into consideration.The UAC centered view provides a coherent and complete summary about features and characteristics of online users(their activities),approaches to detect bullying users(and malicious content),and helps defend cyberspace from bullying and toxicity.

Consensus-Based Ensemble Model for Arabic Cyberbullying Detection

Due to the proliferation of internet-enabled smartphones,many people,particularly young people in Arabic society,have widely adopted social media platforms as a primary means of communication,interaction and friendship mak-ing.The technological advances in smartphones and communication have enabled young people to keep in touch and form huge social networks from all over the world.However,such networks expose young people to cyberbullying and offen-sive content that puts their safety and emotional well-being at serious risk.Although,many solutions have been proposed to automatically detect cyberbully-ing,most of the existing solutions have been designed for English speaking con-sumers.The morphologically rich languages-such as the Arabic language-lead to data sparsity problems.Thus,render solutions developed for another language are ineffective once applied to the Arabic language content.To this end,this study focuses on improving the efficacy of the existing cyberbullying detection models for Arabic content by designing and developing a Consensus-based Ensemble Cyberbullying Detection Model.A diverse set of heterogeneous classifiers from the traditional machine and deep learning technique have been trained using Arabic cyberbullying labeled dataset collected fromfive different platforms.The outputs of the selected classifiers are combined using consensus-based decision-making in which the F1-Score of each classifier was used to rank the classifiers.Then,the Sigmoid function,which can reproduce human-like decision making,is used to infer thefinal decision.The outcomes show the efficacy of the proposed model comparing to the other studied classifiers.The overall improvement gained by the proposed model reaches 1.3%comparing with the best trained classifier.Besides its effectiveness for Arabic language content,the proposed model can be generalized to improve cyberbullying detection in other languages.

Integrated Approach to Detect Cyberbullying Text:Mobile Device Forensics Data

Mobile devices and social networks provide communication opportunities among the young generation,which increases vulnerability and cybercrimes activities.A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among youngsters.This paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics toolkit.We describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying detection.We use forensics techniques,Machine Learning(ML),and Deep Learning(DL)algorithms to exploit suspicious patterns to help the forensics investigation where every evidence contributes to the case.Experiments on a real-time dataset reveal better results for the detection of cyberbullying content.The Random Forest in ML approach produces 87%of accuracy without SMOTE technique,whereas the value of F1Score produces a good result with SMOTE technique.The LSTM has 92%of validation accuracy in the DL algorithm compared with Dense and BiLSTM algorithms.

The Relationship between Cyberbullying Victimization and Depression: The Moderating Effects of Gender and Age

Using cross-sectional, self-report data this study investigates the effect of cyberbullying victimization on depression with gender and age as moderating factors. The sample (n = 1400) consisted of students in a large public university in Kuwait. First, the results show that gender was a significant predictor of depression with females being higher than males on this measure. Second, age was a significant negative predictor with older subjects less likely than younger ones to experience depression. Finally, the study examined the interaction effect of gender and age with cyber-victimization. Results indicate that there is no significant interaction effect between gender and cyber-victimization. However, a significant interaction effect exists between age and cyber-victimization. Older subjects are likely to suffer more depression when exposed to cyber-victimization than younger ones.

Prevalence and Prevention Strategies of Cyberbullying among Nigerian Students

Cyberbullying is a problem that has emerged as a byproduct of modern-day technologies. This form of aggression occurs when one or more individuals use a technological medium for the purpose of intimidating or harming others. In spite of the popularity of technological devices among Nigerian youths presently, there remains a critical gap in literature relating to cyberbullying and its possible effects on students in Nigeria. This study thus sought to identify if a relationship exists between traditional bullying and cyberbullying; examine the effect of cyberbullying on students’ psychological behavior;and examine ways in which cyberbullying might be prevented. The sample consists of students from tertiary institutions while the results were analyzed using both descriptive and inferential statistics. The result of the findings showed that significant correlations were found between traditional bullies and cyberbullies (rranks = 0.322, p < 0.001), cyberbullies and cyberbullied victim (rranks = 0.401, p < 0.0001). There were also significant correlations found between bullies and bully victims (rranks = 0.326, p < 0.001) and between bullied victim and cyberbully victim (rranks = 0.160, p < 0.05). These findings suggest that traditional bullying and cyberbullying share a strong relationship with one another. Furthermore, it was also discovered that those who were victimized through traditional bullying or cyberbullying were also likely to bully others. The result showed that cyberbullied victims often exhibit a variety of negative outcomes especially anger, embarrassment, fear and anxiety. A Spearman Rank-Order correlation revealed a negative association between grade level and cyberbullies (rranks = 0.034, p = 0.355) as well as grade level and cyberbully victims (rranks = 0.107, p = 0.217). A significantly positive relationship occurred between frequency of computer use and cyberbullies (rranks = 0.206, p = 0.015), as well as between frequency of computer use and electronic victimization (rranks = 0.223, p = 0.012). The study concluded that parents, school and mental health providers must not only be aware of cyberbullying and its consequences, but must also have access to ways to deal with this growing concern through public awareness building, anger management training for youths and the establishment of mentorship programs for youths to help one other.

AMachine Learning Approach to Cyberbullying Detection in Arabic Tweets

With the rapid growth of internet usage,a new situation has been created that enables practicing bullying.Cyberbullying has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and fear.With the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current study.This study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the datasets.Moreover,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic language.As a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random Forest(RF),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar problems.Finally,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and F1-score.Consequently,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

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.

Spotted Hyena Optimizer with Deep Learning Driven Cybersecurity for Social Networks

Recent developments on Internet and social networking have led to the growth of aggressive language and hate speech.Online provocation,abuses,and attacks are widely termed cyberbullying(CB).The massive quantity of user generated content makes it difficult to recognize CB.Current advancements in machine learning(ML),deep learning(DL),and natural language processing(NLP)tools enable to detect and classify CB in social networks.In this view,this study introduces a spotted hyena optimizer with deep learning driven cybersecurity(SHODLCS)model for OSN.The presented SHODLCS model intends to accomplish cybersecurity from the identification of CB in the OSN.For achieving this,the SHODLCS model involves data pre-processing and TF-IDF based feature extraction.In addition,the cascaded recurrent neural network(CRNN)model is applied for the identification and classification of CB.Finally,the SHO algorithm is exploited to optimally tune the hyperparameters involved in the CRNN model and thereby results in enhanced classifier performance.The experimental validation of the SHODLCS model on the benchmark dataset portrayed the better outcomes of the SHODLCS model over the recent approaches.

The Study of Cyber-bullying from the Perspective of Critical Discourse Analysis:A Case Study of Tik Tok Comment Area Language

With the spread of the Internet,cyberbullying can a lso be seen in cyberspace.This will not only destroy our network usage environment,but also affect the harmonious development of society.Due to the lack of attention to the process of cyberbullying,many previous researches have been conducted only in t erms of causes and strategies,and the relationship between cyberbullying and ideology has been overlooked.Therefore,this study collects Tik Tok comments and builds a corpus to study the relationship between cyberbullying and ideology from the perspectiv e of critical discourse analysis.

Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

Cyberbullying(CB)is a distressing online behavior that disturbs mental health significantly.Earlier studies have employed statistical and Machine Learning(ML)techniques for CB detection.With this motivation,the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification(ODL-CDC)technique for CB detection in social networks.The proposed ODL-CDC technique involves different processes such as pre-processing,prediction,and hyperparameter optimization.In addition,GloVe approach is employed in the generation of word embedding.Besides,the pre-processed data is fed into BidirectionalGated Recurrent Neural Network(BiGRNN)model for prediction.Moreover,hyperparameter tuning of BiGRNN model is carried out with the help of Search and Rescue Optimization(SRO)algorithm.In order to validate the improved classification performance of ODL-CDC technique,a comprehensive experimental analysis was carried out upon benchmark dataset and the results were inspected under varying aspects.A detailed comparative study portrayed the superiority of the proposed ODL-CDC technique over recent techniques,in terms of performance,with the maximum accuracy of 92.45%.

Perception,use of social media,and its impact on the mental health of Indian adolescents:A qualitative study

BACKGROUND Mental illness is a health challenge faced by adolescents that has grown worse after the Coronavirus disease 2019 pandemic.Research on social media and young people’s mental health has recently increased,and numerous studies have examined whether frequent use of social media is linked to issues such as anxiety,stress,depression,eating disorders,insomnia,frustration,feeling alone,and externalizing problems among adolescents.This influence of social media on adolescents’lives is clear,with many platforms like Facebook,Instagram,and YouTube playing an important role in daily interactions and self-expression.Even though social media offers numerous benefits,such as connectivity and information sharing,excessive usage can have detrimental effects on mental health,particularly among adolescents.AIM To study the impact of social media on the mental wellbeing of adolescents,and the associated potential dangers in India.METHODS A total of 204 adolescents aged 14 years to 23 years were included in the study.This study explored the intricate relationship between social media usage and adolescent mental health in India.The study employs a cross-sectional survey design to capture a snapshot of adolescent mental health and social media usage patterns.Data collection involved administering structured questionnaires and the analysis utilized quantitative methods,including descriptive statistics.RESULTS Excessive use of social media is correlated with increased stress,anxiety,and depression.Adolescents engage in compulsive behaviors such as scrolling in the middle of the night,which negatively impacts their mental and physical health,and leads to significant sleep disruption.Findings from the study aim to provide insights into the current state of adolescent mental health and inform strategies to promote positive wellbeing in the Indian population.CONCLUSION The study underscores the need for further research to better understand the complex interplay between social media and adolescent mental health,and need for effective strategies to combat online harassment.