Predicting Cyberbullying

Predicting Cyberbullying: Research, Theory, and Intervention delves into the theoretical advances that have been made to predict cyberbullying perpetration. It examines myriad psychological- and communication-based theories, discusses the relevant research to support (or not) each theory, and elucidates the strengths and limitations of these theories. Moreover, the book differentiates cyberbullying from traditional bullying to expand on a theory that takes such differences into account to predict perpetration. In addition, it adapts interventions to address these nuanced theoretical advancements and concludes with an examination of validated psychological theories that can inform interventions and reduce cyberbullying. The book is an effective and concise reference for psychologists, school administrators, counselors and psychological researchers looking to understand theory and interventions for cyberbullies. Focuses on the cyberbully perpetrator Balances theory with interventional applications Identifies key risk factors in those who cyberbully Explores the scope of theoretically driven hypotheses specific to cyberbullying

Cyberbullying in the Global Playground provides the first global, in-depth analysis of the emerging phenomenon of cyberbullying. Offers the first thorough comparative account of recent research into the emerging global phenomenon of cyberbullying Provides an international perspective on the prevalence and nature of cyberbullying Presents recent authoritative research within a critical perspective, drawing out theoretical and practical implications for policy and practice May be used to help design intervention, evaluation, and policy strategies for effective efforts to combat the international phenomenon of cyberbullying

PROJECT 1: SUPERMARKET SALES ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project consists of the growth of supermarkets with high market competitions in most populated cities. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data. Predictive data analytics methods are easy to apply with this dataset. Attribute information in the dataset are as follows: Invoice id: Computer generated sales slip invoice identification number; Branch: Branch of supercenter (3 branches are available identified by A, B and C); City: Location of supercenters; Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card; Gender: Gender type of customer; Product line: General item categorization groups - Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel; Unit price: Price of each product in $; Quantity: Number of products purchased by customer; Tax: 5% tax fee for customer buying; Total: Total price including tax; Date: Date of purchase (Record available from January 2019 to March 2019); Time: Purchase time (10am to 9pm); Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and Ewallet); COGS: Cost of goods sold; Gross margin percentage: Gross margin percentage; Gross income: Gross income; and Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10). In this project, you will perform predicting rating using machine learning. The machine learning models used in this project to predict clusters as target variable are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, LGBM, Gradient Boosting, XGB, and MLP. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 2: DETECTING CYBERBULLYING TWEETS USING MACHINE LEARNING AND DEEP LEARNING WITH PYTHON GUI As social media usage becomes increasingly prevalent in every age group, a vast majority of citizens rely on this essential medium for day-to-day communication. Social media’s ubiquity means that cyberbullying can effectively impact anyone at any time or anywhere, and the relative anonymity of the internet makes such personal attacks more difficult to stop than traditional bullying. On April 15th, 2020, UNICEF issued a warning in response to the increased risk of cyberbullying during the COVID-19 pandemic due to widespread school closures, increased screen time, and decreased face-to-face social interaction. The statistics of cyberbullying are outright alarming: 36.5% of middle and high school students have felt cyberbullied and 87% have observed cyberbullying, with effects ranging from decreased academic performance to depression to suicidal thoughts. In light of all of this, this dataset contains more than 47000 tweets labelled according to the class of cyberbullying: Age; Ethnicity; Gender; Religion; Other type of cyberbullying; and Not cyberbullying. The data has been balanced in order to contain ~8000 of each class. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, XGB classifier, LSTM, and CNN. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 3: HIGHER EDUCATION STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset used in this project was collected from the Faculty of Engineering and Faculty of Educational Sciences students in 2019. The purpose is to predict students' end-of-term performances using ML techniques. Attribute information in the dataset are as follows: Student ID; Student Age (1: 18-21, 2: 22-25, 3: above 26); Sex (1: female, 2: male); Graduated high-school type: (1: private, 2: state, 3: other); Scholarship type: (1: None, 2: 25%, 3: 50%, 4: 75%, 5: Full); Additional work: (1: Yes, 2: No); Regular artistic or sports activity: (1: Yes, 2: No); Do you have a partner: (1: Yes, 2: No); Total salary if available (1: USD 135-200, 2: USD 201-270, 3: USD 271-340, 4: USD 341-410, 5: above 410); Transportation to the university: (1: Bus, 2: Private car/taxi, 3: bicycle, 4: Other); Accommodation type in Cyprus: (1: rental, 2: dormitory, 3: with family, 4: Other); Mother's education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.); Father's education: (1: primary school, 2: secondary school, 3: high school, 4: university, 5: MSc., 6: Ph.D.); Number of sisters/brothers (if available): (1: 1, 2:, 2, 3: 3, 4: 4, 5: 5 or above); Parental status: (1: married, 2: divorced, 3: died - one of them or both); Mother's occupation: (1: retired, 2: housewife, 3: government officer, 4: private sector employee, 5: self-employment, 6: other); Father's occupation: (1: retired, 2: government officer, 3: private sector employee, 4: self-employment, 5: other); Weekly study hours: (1: None, 2: <5 hours, 3: 6-10 hours, 4: 11-20 hours, 5: more than 20 hours); Reading frequency (non-scientific books/journals): (1: None, 2: Sometimes, 3: Often); Reading frequency (scientific books/journals): (1: None, 2: Sometimes, 3: Often); Attendance to the seminars/conferences related to the department: (1: Yes, 2: No); Impact of your projects/activities on your success: (1: positive, 2: negative, 3: neutral); Attendance to classes (1: always, 2: sometimes, 3: never); Preparation to midterm exams 1: (1: alone, 2: with friends, 3: not applicable); Preparation to midterm exams 2: (1: closest date to the exam, 2: regularly during the semester, 3: never); Taking notes in classes: (1: never, 2: sometimes, 3: always); Listening in classes: (1: never, 2: sometimes, 3: always); Discussion improves my interest and success in the course: (1: never, 2: sometimes, 3: always); Flip-classroom: (1: not useful, 2: useful, 3: not applicable); Cumulative grade point average in the last semester (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49); Expected Cumulative grade point average in the graduation (/4.00): (1: <2.00, 2: 2.00-2.49, 3: 2.50-2.99, 4: 3.00-3.49, 5: above 3.49); Course ID; and OUTPUT: Grade (0: Fail, 1: DD, 2: DC, 3: CC, 4: CB, 5: BB, 6: BA, 7: AA). The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 4: COMPANY BANKRUPTCY ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI The dataset was collected from the Taiwan Economic Journal for the years 1999 to 2009. Company bankruptcy was defined based on the business regulations of the Taiwan Stock Exchange. Attribute information in the dataset are as follows: Y - Bankrupt?: Class label; X1 - ROA(C) before interest and depreciation before interest: Return On Total Assets(C); X2 - ROA(A) before interest and % after tax: Return On Total Assets(A); X3 - ROA(B) before interest and depreciation after tax: Return On Total Assets(B); X4 - Operating Gross Margin: Gross Profit/Net Sales; X5 - Realized Sales Gross Margin: Realized Gross Profit/Net Sales; X6 - Operating Profit Rate: Operating Income/Net Sales; X7 - Pre-tax net Interest Rate: Pre-Tax Income/Net Sales; X8 - After-tax net Interest Rate: Net Income/Net Sales; X9 - Non-industry income and expenditure/revenue: Net Non-operating Income Ratio; X10 - Continuous interest rate (after tax): Net Income-Exclude Disposal Gain or Loss/Net Sales; X11 - Operating Expense Rate: Operating Expenses/Net Sales; X12 - Research and development expense rate: (Research and Development Expenses)/Net Sales X13 - Cash flow rate: Cash Flow from Operating/Current Liabilities; X14 - Interest-bearing debt interest rate: Interest-bearing Debt/Equity; X15 - Tax rate (A): Effective Tax Rate; X16 - Net Value Per Share (B): Book Value Per Share(B); X17 - Net Value Per Share (A): Book Value Per Share(A); X18 - Net Value Per Share (C): Book Value Per Share(C); X19 - Persistent EPS in the Last Four Seasons: EPS-Net Income; X20 - Cash Flow Per Share; X21 - Revenue Per Share (Yuan ¥): Sales Per Share; X22 - Operating Profit Per Share (Yuan ¥): Operating Income Per Share; X23 - Per Share Net profit before tax (Yuan ¥): Pretax Income Per Share; X24 - Realized Sales Gross Profit Growth Rate; X25 - Operating Profit Growth Rate: Operating Income Growth; X26 - After-tax Net Profit Growth Rate: Net Income Growth; X27 - Regular Net Profit Growth Rate: Continuing Operating Income after Tax Growth; X28 - Continuous Net Profit Growth Rate: Net Income-Excluding Disposal Gain or Loss Growth; X29 - Total Asset Growth Rate: Total Asset Growth; X30 - Net Value Growth Rate: Total Equity Growth; X31 - Total Asset Return Growth Rate Ratio: Return on Total Asset Growth; X32 - Cash Reinvestment %: Cash Reinvestment Ratio X33 - Current Ratio; X34 - Quick Ratio: Acid Test; X35 - Interest Expense Ratio: Interest Expenses/Total Revenue; X36 - Total debt/Total net worth: Total Liability/Equity Ratio; X37 - Debt ratio %: Liability/Total Assets; X38 - Net worth/Assets: Equity/Total Assets; X39 - Long-term fund suitability ratio (A): (Long-term Liability+Equity)/Fixed Assets; X40 - Borrowing dependency: Cost of Interest-bearing Debt; X41 - Contingent liabilities/Net worth: Contingent Liability/Equity; X42 - Operating profit/Paid-in capital: Operating Income/Capital; X43 - Net profit before tax/Paid-in capital: Pretax Income/Capital; X44 - Inventory and accounts receivable/Net value: (Inventory+Accounts Receivables)/Equity; X45 - Total Asset Turnover; X46 - Accounts Receivable Turnover; X47 - Average Collection Days: Days Receivable Outstanding; X48 - Inventory Turnover Rate (times); X49 - Fixed Assets Turnover Frequency; X50 - Net Worth Turnover Rate (times): Equity Turnover; X51 - Revenue per person: Sales Per Employee; X52 - Operating profit per person: Operation Income Per Employee; X53 - Allocation rate per person: Fixed Assets Per Employee; X54 - Working Capital to Total Assets; X55 - Quick Assets/Total Assets; X56 - Current Assets/Total Assets; X57 - Cash/Total Assets; X58 - Quick Assets/Current Liability; X59 - Cash/Current Liability; X60 - Current Liability to Assets; X61 - Operating Funds to Liability; X62 - Inventory/Working Capital; X63 - Inventory/Current Liability X64 - Current Liabilities/Liability; X65 - Working Capital/Equity; X66 - Current Liabilities/Equity; X67 - Long-term Liability to Current Assets; X68 - Retained Earnings to Total Assets; X69 - Total income/Total expense; X70 - Total expense/Assets; X71 - Current Asset Turnover Rate: Current Assets to Sales; X72 - Quick Asset Turnover Rate: Quick Assets to Sales; X73 - Working capitcal Turnover Rate: Working Capital to Sales; X74 - Cash Turnover Rate: Cash to Sales; X75 - Cash Flow to Sales; X76 - Fixed Assets to Assets; X77 - Current Liability to Liability; X78 - Current Liability to Equity; X79 - Equity to Long-term Liability; X80 - Cash Flow to Total Assets; X81 - Cash Flow to Liability; X82 - CFO to Assets; X83 - Cash Flow to Equity; X84 - Current Liability to Current Assets; X85 - Liability-Assets Flag: 1 if Total Liability exceeds Total Assets, 0 otherwise; X86 - Net Income to Total Assets; X87 - Total assets to GNP price; X88 - No-credit Interval; X89 - Gross Profit to Sales; X90 - Net Income to Stockholder's Equity; X91 - Liability to Equity; X92 - Degree of Financial Leverage (DFL); X93 - Interest Coverage Ratio (Interest expense to EBIT); X94 - Net Income Flag: 1 if Net Income is Negative for the last two years, 0 otherwise; and X95 - Equity to Liabilitys. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy. PROJECT 5: DATA SCIENCE FOR RAIN CLASSIFICATION AND PREDICTION WITH PYTHON GUI This dataset contains about 10 years of daily weather observations from many locations across Australia. RainTomorrow is the target variable to predict. You will determine rain or not in the next day. This column is Yes if the rain for that day was 1mm or more. Observations were drawn from numerous weather stations. The daily observations are available from http://www.bom.gov.au/climate/data. The dataset contains 23 attributes. Some of them are as follows: About some of them are: DATE - The date of observation; LOCATION - The common name of the location of the weather station; MINTEMP - The minimum temperature in degrees celsius; MAXTEMP - The maximum temperature in degrees celsius; RAINFALL - The amount of rainfall recorded for the day in mm; EVAPORATION - The so-called Class A pan evaporation (mm) in the 24 hours to 9am; SUNSHINE - The number of hours of bright sunshine in the day; WINDGUESTDIR - The direction of the strongest wind gust in the 24 hours to midnight; WINDGUESTSPEED- The speed (km/h) of the strongest wind gust in the 24 hours to midnight; and WINDDIR9AM - Direction of the wind at 9am. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier. Three feature scaling used in machine learning are raw, minmax scaler, and standard scaler. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, decision boundaries, performance of the model, scalability of the model, training loss, and training accuracy.

This project focuses on detecting cyberbullying tweets using both Machine Learning and Deep Learning techniques with a Python GUI implemented using PyQt. The first step involves data exploration, where the dataset is loaded and analyzed to gain insights into its structure and contents. Visualizations are created to understand the distribution of cyberbullying types and other features in the data. After data exploration, preprocessing is performed to clean and prepare the tweets for analysis. Text cleaning techniques, such as removing emojis, punctuation, links, and stop words, are applied to the tweet text. The data is then categorized based on the length of the tweets to facilitate further analysis. Next, the data is split into input and output variables, where the tweet text becomes the input feature (X) and the cyberbullying type becomes the output (y). The cyberbullying types are converted into numerical labels for ML models' compatibility. Machine Learning models are trained and evaluated using TF-IDF, Count Vectorizer, and Hashing Vectorizer as feature extraction techniques. SMOTE is applied to handle class imbalance, and the data is split into training and testing sets. Grid Search is utilized to find the best hyperparameters for ML models, optimizing their performance. Machine Learning models used are Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting. Moving to Deep Learning, LSTM (Long Short-Term Memory) and 1D CNN (Convolutional Neural Network) models are constructed to detect cyberbullying types. The tweet text is embedded, and various layers are added to the models to extract meaningful features. The models are then compiled with appropriate loss functions and optimizers. The evaluation process is carried out using the test set for both Machine Learning and Deep Learning models. Metrics like accuracy, precision, recall, and F1-score are used to assess the models' performance in detecting cyberbullying types. To enable easy access to the functionalities, a Graphical User Interface (GUI) is developed using PyQt. The GUI allows users to interact with the models and dataset easily. Users can select the feature extraction technique, choose the classifier, and initiate the model training process using the GUI's buttons and dropdown menus. For better data visualization, the GUI includes various plots, such as bar plots and pie charts, to show the distribution of cyberbullying types and other features. These visualizations help users understand the data and model performance intuitively. The final stage involves testing the GUI with different inputs and exploring model predictions on user-provided text. The GUI provides predictions for the given tweet text, indicating the likelihood of each cyberbullying type based on the trained models. Overall, this project combines data exploration, preprocessing, feature extraction using Machine Learning and Deep Learning models, GUI development, and data visualization to detect cyberbullying tweets effectively and provide an accessible interface for users to interact with the models. The end product allows users to analyze tweets for potential cyberbullying and contributes to promoting a safer and more respectful online environment.

Given users’ heavy reliance of modern communication technologies such as mobile and tablet devices, laptops, computers, and social media networks, workplace cyberbullying and online harassment have become escalating problems around the world. Organizations of all sizes and sectors (public and private) may encounter workplace cyberbullying within and outside the boundaries of physical offices. Workplace cyberbullying affects the entire company, as victims suffer from psychological trauma and mental health issues that can lead to anxiety and depression, which, in turn, can cause absenteeism, job turnover, and retaliation. Thus, businesses must develop effective strategies to prevent and resolve such issues from becoming too large to manage. The Handbook of Research on Cyberbullying and Online Harassment in the Workplace provides in-depth research that explores the theoretical and practical measures of managing bullying behaviors within an organization as well as the intervention strategies that should be employed. The book takes a look at bullying behavior across a variety of industries, including government and educational institutions, and examines social and legislative issues, policies and legal cases, the impact of online harassment and disruption of business processes and organizational culture, and prevention techniques. Featuring coverage on a broad range of topics such as sexual abuse and trolling, this book is ideally designed for business managers and executives, human resource managers, practitioners, policymakers, academicians, researchers, and students.

Cyberbullying and Online Harms identifies online harms and their impact on young people, from communities to campuses, exploring current and future interventions to reduce and prevent online harassment and aggression. This important resource brings together eminent international researchers whose work shines a light on social issues such as bullying/cyberbullying, racism, homophobia, hate crime, and social exclusion. The text collates into one volume current knowledge and evidence of cyberbullying and its effect on young people, facilitating action to protect victims, challenge perpetrators and develop policies and practices to change cultures that are discriminatory and divisive. It also provides a space where those who have suffered online harms and who have often been silenced in the past may have a voice in telling their experiences and recounting interventions and policies that helped them to create safer spaces in which to live in their community, study in their educational institutions and socialise with their peer group. This is essential reading for researchers, academics, undergraduates and postgraduates in sociology, psychology, criminology, media and communication studies, as well as practitioners and policymakers in psychology, education, sociology, criminology, psychiatry, counselling and psychotherapy, and anyone concerned with the issue of bullying, cyberbullying and online harms among young people in higher education.

Child and Adolescent Online Risk Exposure: An Ecological Perspective focuses on online risks and outcomes for children and adolescents using an ecological perspective (i.e., the intersection of individuals in relevant contexts) for a better understanding of risks associated with the youth online experience. The book examines the specific consequences of online risks for youth and demonstrates how to develop effective and sensitive interventions and policies. Sections discuss why online risks are important, individual and contextual factors, different types of risk, online risks among special populations, such as LGBT youth, physically or intellectually disabled youth, and ethnic and religious minorities, and intervention efforts. Examines online risks such as problematic internet use, contact risk behaviors, online exploitation, online hate, cyberbullying, and cyberstalking Explores the concept of digital citizenship Includes theoretical considerations and the prevalence of online risks Covers policy and intervention recommendations for reducing online risks

Written by scholars from both the Western and Chinese contexts, this monograph discusses the relation between cyberbullying and socio-emotional-moral competencies, feasible interventions by integrating values education, and provides future directions in the field of cyberpsychology. Cyberbullying has become a growing concern in the digital age as it brings devastating impacts on its victims. Educating the younger generation, particularly through values education, also known as character, moral, or social-emotional learning, helps equip children and adolescents with the necessary ethical and moral attitudes, and foster the necessary socio-emotional competencies for them to navigate the digital world as responsible cyber-citizens. A central focus of the book is intervention and education. Cultivating competences and responsible use of technology in the younger generation through values education and evidence-based intervention helps combat cyberbullying. Families, schools, and communities can work together with suitable school programs, teacher education, and parents/school collaboration to help students cope with cyberbullying and create safer online spaces for them. Technology itself is not inherently good or bad but shaped by human choices and values. Supported by empirical evidence and theoretical insights, this book suggests ways to promote moral and emotional skills, foster digital citizenship, and encourage ethical technology design. This book provides a comprehensive understanding of cyberbullying. This timely resource will contribute to creating a safer and more positive online environment for all. It will inform researchers, educators, parents, and the community in combating cyberbullying by enabling children and adolescents to be responsible, ethical, and happy netizens.