Detection and Prevention of Cyberbullying on Social Media

The Internet and social media have undoubtedly improved our abilities to keep in touch with friends and loved ones. Additionally, it has opened up new avenues for journalism, activism, commerce and entertainment. The unbridled ubiquity of social media is, however, not without negative consequences and one such effect is the increased prevalence of cyberbullying and online abuse. While cyberbullying was previously restricted to electronic mail, online forums and text messages, social media has propelled it across the breadth of the Internet, establishing it as one of the main dangers associated with online interactions. Recent advances in deep learning algorithms have progressed the state of the art in natural language processing considerably, and it is now possible to develop Machine Learning (ML) models with an in-depth understanding of written language and utilise them to detect cyberbullying and online abuse. Despite these advances, there is a conspicuous lack of real-world applications for cyberbullying detection and prevention. Scalability; responsiveness; obsolescence; and acceptability are challenges that researchers must overcome to develop robust cyberbullying detection and prevention systems. This research addressed these challenges by developing a novel mobile-based application system for the detection and prevention of cyberbullying and online abuse. The application mitigates obsolescence by using different ML models in a “plug and play” manner, thus providing a mean to incorporate future classifiers. It uses ground truth provided by the enduser to create a personalised ML model for each user. A new large-scale cyberbullying dataset of over 62K tweets annotated using a taxonomy of different cyberbullying types was created to facilitate the training of the ML models. Additionally, the design incorporated facilities to initiate appropriate actions on behalf of the user when cyberbullying events are detected. To improve the app’s acceptability to the target audience, user-centred design methods were used to discover stakeholders’ requirements and collaboratively design the mobile app with young people. Overall, the research showed that (a) the cyberbullying dataset sufficiently captures different forms of online abuse to allow the detection of cyberbullying and online abuse; (b) the developed cyberbullying prevention application is highly scalable and responsive and can cope with the demands of modern social media platforms (b) the use of user-centred and participatory design approaches improved the app’s acceptability amongst the target audience

Approaches to automated detection of cyberbullying:A Survey

Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilise machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. In this paper, we present a systematic review of published research (as identified via Scopus, ACM and IEEE Xplore bibliographic databases) on cyberbullying detection approaches. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely; supervised learning, lexicon based, rule based and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naïve Bayes to develop predictive models for cyberbullying detection. Lexicon based systems utilise word lists and use the presence of words within the lists to detect cyberbullying. Rules-based approaches match text to predefined rules to identify bullying and mixed-initiatives approaches combine human-based reasoning with one or more of the aforementioned approaches. We found lack of quality representative labelled datasets and non-holistic consideration of cyberbullying by researchers when developing detection systems are two key challenges facing cyberbullying detection research. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in this field

IDENTIFICATION OF MATHEMATICAL ERRORS COMMITTED BY SENIOR SCHOOL STUDENTS IN CALCULUS

 AbstractThis study aimed to identify mathematical errors committed by senior school students in calculus. Specifically, this study analyzes the various types of errors committed when solving problems involving calculus. The study is a descriptive study that employed a mathematics performance test on calculus (MPT-C) as the research instrument. The study considered two independent variables which are: error type and gender. The research sample for the study comprised all senior secondary school three (SS III) students. A random sampling technique was used to select the participating schools. A total of 300 senior secondary school students were involved in the selected schools. All research questions were answered using mean gain difference while all research hypotheses were tested using chi-square. All the research hypotheses were tested at a 0.05-significance level. The results of the analysis indicated that: there were no significant differences in the frequencies of structural, supposition, translation, operation, and random errors committed by male and female senior secondary school students in word problem-solving involving calculus. Keywords:  calculus; error; identification; gender; mathematics AbstrakPenelitian ini bertujuan untuk mengidentifikasi kesalahan matematika yang dilakukan oleh siswa sekolah menengah atas dalam materi kalkulus. Secara khusus, penelitian ini menganalisis berbagai jenis kesalahan yang dilakukan ketika memecahkan masalah yang melibatkan kalkulus. Penelitian ini merupakan penelitian deskriptif dengan instrumen penelitian berupa tes kinerja matematika pada kalkulus. Penelitian ini mempertimbangkan dua variabel independen yaitu: jenis kesalahan dan jenis kelamin. Sampel penelitian untuk penelitian ini adalah seluruh siswa sekolah menengah atas (SS III). Teknik pengambilan sampel secara acak digunakan untuk memilih sekolah yang berpartisipasi. Sebanyak 300 siswa sekolah menengah atas terlibat di sekolah-sekolah yang terpilih. Semua pertanyaan penelitian dijawab dengan menggunakan rata-rata perbedaan gain, sedangkan semua hipotesis penelitian diuji menggunakan chi-square. Semua hipotesis penelitian diuji pada tingkat signifikansi 0,05. Hasil analisis menunjukkan bahwa: tidak ada perbedaan yang signifikan dalam frekuensi kesalahan struktural, anggapan, terjemahan, operasi, dan acak yang dilakukan oleh siswa sekolah menengah laki-laki dan perempuan dalam pemecahan masalah kata yang melibatkan kalkulus.Kata Kunci: identifikasi; jenis kelamin; kalkulus; kesalahan; matematika  DOI: http://dx.doi.org/10.23960/mtk/v10i2.pp106-12

A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection

In this paper, we introduce a new English Twitter-based dataset for online abuse and cyberbullying detection. Comprising 62,587 tweets, this dataset was sourced from Twitter using specific query terms designed to retrieve tweets with high probabilities of various forms of bullying and offensive content, including insult, profanity, sarcasm, threat, porn and exclusion. Analysis performed on the dataset confirmed common cyberbullying themes reported by other studies and revealed interesting relationships between the classes. The dataset was used to train a number of transformer-based deep learning models returning impressive results

BullStop: A Mobile App for Cyberbullying Prevention

Social media has become the new playground for bullies. Young people are now regularly exposed to a wide range of abuse online. In response to the increasing prevalence of cyberbullying, online social networks have increased efforts to clamp down on online abuse but unfortunately, the nature, complexity and sheer volume of cyberbullying means that many cyberbullying incidents go undetected. BullStop is a mobile app for detecting and preventing cyberbullying and online abuse on social media platforms. It uses deep learning models to identify instances of cyberbullying and can automatically initiate actions such as deleting offensive messages and blocking bullies on behalf of the user. Our system not only achieves impressive prediction results but also demonstrates excellent potential for use in real-world scenarios and is freely available on the Google Play Store

EFFECTS OF PROBLEM-BASED INSTRUCTIONAL STRATEGY ON SENIOR SCHOOL STUDENTS’ PERFORMANCE IN CIRCLE THEOREMS

AbstractThis study was conducted with the aim of determining the effects of Problem-Based Instructional Strategy (PBIS) on students’ performance in circle theorems in Mathematics. The study is a quasi-experimental type of non-randomized, nonequivalent pre-test and post-test control group involving a 2 x 2 x 3 factorial design, indicating instructional strategies at two levels (Problem-Based Instructional Strategy and conventional method), gender at two levels (male and female) and scoring level at three levels (high, moderate and low level). The target population was all Senior Secondary School students in Ogbomoso, Nigeria. A total of 244 students participated in the study. The research instrument used is Circle Theorems Performance Test (CIRTPT). t-test statistics and ANCOVA were employed to analyze the data. The findings of the study revealed that: there was a significant difference in the performance of students taught circle theorems using PBIS and those taught with conventional method (t(242)=2.87; p0.05); there was no significant difference in the performance of male and female students taught circle theorems using PBIS (t(110)=0.52; p0.05); and there was no significant difference in the performance of students on the basis of score levels when taught circle theorems using PBIS (F(2,108)=2.31; p .05). Keywords: circle; gender; performance; problem-based learning AbstrakTujuan penelitian ini adalah untuk mengetahui pengaruh Strategi Problem Based Learning (PBM) terhadap kinerja siswa terkait teorema lingkaran dalam Matematika. Penelitian ini merupakan jenis eksperimen semu dengan jenis kelompok kontrol pre-test dan post-test non-randomized, nonequivalent yang melibatkan desain faktorial 2 x 2 x 3, yang menunjukkan strategi pembelajaran pada dua tingkat (Strategi Pembelajaran Berbasis Masalah dan metode konvensional), gender pada dua tingkat (laki-laki dan perempuan) dan tingkat penilaian pada tiga tingkat (tingkat tinggi, sedang dan rendah). Populasi dalam penelitian ini adalah semua siswa Sekolah Menengah Atas di Ogbomoso, Nigeria. Sebanyak 244 siswa berpartisipasi dalam penelitian ini. Instrumen penelitian yang digunakan adalah Circle Theorems Performance Test (CIRTPT). Statistik uji-t dan ANCOVA digunakan untuk menganalisis data. Hasil penelitian menunjukkan bahwa: terdapat perbedaan yang signifikan kinerja siswa yang diajar teorema lingkaran menggunakan PBM dan siswa yang diajar dengan metode konvensional (t(242)=2,87; p0,05); tidak ada perbedaan yang signifikan dalam kinerja siswa laki-laki dan perempuan yang diajarkan teorema lingkaran menggunakan PBM (t(110)=0,52; p0,05); dan tidak ada perbedaan yang signifikan dalam kinerja siswa berdasarkan tingkat skor ketika diajarkan teorema lingkaran menggunakan PBM (F(2,108)=2,31; p 0,05).Kata kunci: gender; kinerja; lingkaran; problem based learning  DOI: http://dx.doi.org/10.23960/mtk/v10i1.pp1-1

A mobile-based system for preventing online abuse and cyberbullying

A negative consequence of the proliferation of social media is the increase in online abuse. Bullying, once restricted to the playground, has found a new home on social media. Online social networks on their part have intensified efforts to tackle online abuse, but unfortunately, such is the scale of the problem that many young people are still regularly subjected to a wide range of abuse online. Research in automated detection of online abuse has increased considerably in recent times. However, existing studies on online abuse detection typically focus on developing newer algorithms to improve predictions, and little research is done on developing impactful tools that leverage these algorithms to tackle online abuse. In this paper, we present BullStop, a mobile application that can use different machine learning models to detect cyberbullying. A new cyberbullying dataset containing 62,587 tweets annotated using a taxonomy of different cyberbullying types was created to facilitate the classifier’s training. BullStop was developed using a participatory and user-centred design approach involving young people, parents, educators, law enforcement and mental health professionals. Additionally, the application incorporates online training for the ML models using ground truth supplied by the user as additional training data, and in this way, it can create a personalised classifier for each user. Furthermore, on detecting online abuse, the application automatically initiates punitive actions such as deleting offensive messages and blocking cyberbullies on behalf of the user. BullStop is freely available on the Google Play Store and has been downloaded by hundreds of users