Cybercrime in Social Media: Theory and Solutions

Description

This reference text presents the important components for grasping the potential of social computing with an emphasis on concerns, challenges, and benefits of the social platform in depth. Features: Detailed discussion on social-cyber issues, including hate speech, cyberbullying, and others Discusses usefulness of social platforms for societal needs Includes framework to address the social issues with their implementations Covers fake news and rumor detection models Describes sentimental analysis of social posts with advanced learning techniques The book is ideal for undergraduate, postgraduate, and research students who want to learn about the issues, challenges, and solutions of social platforms in depth. Cover Half Title Title Page Copyright Page Table of Contents Preface Editors List of Contributors 1 Fundamental Theories Behind the Detection of Fake News and Rumors 1.1 Introduction 1.1.1 Fake News Gain Public Trust, How? 1.2 Definitions 1.3 Fundamental Theories 1.3.1 News-Related Theories 1.3.2 User-Related Theories 1.4 Fake News Detection Mechanisms 1.4.1 Knowledge-Based Fake News Detection 1.4.1.1 (Knowledge-Based) Expert-Based Manual Fact-Checking 1.4.1.2 (Knowledge-Based) Crowd-Sourced Manual Fact-Checking 1.4.1.3 (Knowledge-Based) Automatic Fact-Checking 1.4.2 Style-Based Fake News Detection 1.4.2.1 How to Capture the Style of News in Terms of Features 1.4.2.2 Machine Learning Approaches for Detection 1.4.3 Propagation-Based Fake News Detection 1.4.4 Source-Based Fake News Detection 1.5 Fake News Datasets 1.6 Results and Discussion 1.7 Conclusion References 2 Social Media and Cybercrime: A Sociodemographic Study of Awareness Level Among Indian Youth 2.1 Introduction 2.2 Review of Literature 2.3 Objectives of the Study 2.4 Hypotheses of the Study 2.5 Methodology 2.5.1 Primary Data and Sampling 2.5.2 Research Instrument and Scale Validation 2.5.3 Demographic Profile 2.6 Analysis and Interpretation 2.7 Result and Discussion 2.8 Contribution of the Study 2.9 Conclusion References 3 Emotions Detection From Social Media Text Using Machine Learning 3.1 Introduction 3.1.1 Why Do We Need to Detect Emotions? 3.2 Background and Existing Work 3.2.1 Existing Works 3.2.2 Techniques to Detect the Emotions From Text 3.2.3 Motivation 3.3 Methodology 3.3.1 Data Collection 3.3.2 Preprocessing 3.4 Feature Extraction 3.5 Data Split for Training and Testing 3.6 Classifiers: Machine Learning Models 3.7 Results 3.7.1 Results With Support Vector Machine Classifier 3.7.2 Results With Decision Tree Classifier 3.7.3 Results With Random Forest Classifier 3.7.4 Results With Random Forest Classifier Having Tuned Hyperparameters 3.8 Conclusion Note References 4 Hope Speech Detection On Social Media Platforms 4.1 Introduction 4.2 Related Works 4.2.1 Fake News Detection 4.2.2 Hate Speech Detection 4.2.3 Hope Speech Detection 4.2.4 Cyberbullying and Fake Profile Detection 4.3 Data and Methods 4.3.1 Dataset Description 4.3.2 Revisiting the Dataset 4.3.3 Reasons to Relabel the Dataset 4.3.4 Criteria of Relabelling 4.3.5 Data Pre-Processing 4.3.6 Model Description 4.4 Results 4.5 Discussion 4.6 Conclusion and Future Work References 5 A Multilingual Review of Hate Speech Detection in Social Media Content 5.1 Introduction 5.2 What Is Hate Speech? 5.3 Hate Speech Identification Approaches 5.3.1 Research On Hate Speech and Offensive Language Identification in the English Language 5.3.2 Research On Hate Speech and Offensive Language Identification in Other Language 5.3.3 Research On Hate Speech and Offensive Language Identification in Code-Mixed Language 5.4 Conclusion and Future Directions References 6 Analysis and Detection of Cyberbullying in Reddit Discussions 6.1 Introduction 6.2 Related Works 6.3 Methodology 6.3.1 Dataset Collection, Labelling, and Preprocessing 6.4 Temporal and Graph Properties Analysis 6.4.1 Proposed Approach 6.5 Results and Discussions 6.6 Conclusion and Future Work Notes References 7 A Doubled-Edged Sword Called Cyberspace: Introducing the Concept of Cybercrime 7.1 Introduction 7.2 Review Methodology 7.3 Concept of Cybercrime 7.4 Classification of Cybercrime 7.5 The Impact of Cybercrime 7.6 Conclusion Notes References 8 Cybercrime By Minors 8.1 Introduction 8.2 Methodology 8.2.1 Computer as a Tool and Target 8.2.2 Types of Cybercriminals 8.2.2.1 The Age Group Category 6 to 18 Years Old 8.2.2.2 Organized Hackers 8.2.2.3 Professional Hackers Or the Ethical Hackers 8.2.2.4 Discontented Employees 8.2.3 Types of Cybercrimes 8.2.3.1 Unauthorized Access 8.2.3.2 Cyber Fraud/Online Fraud 8.2.3.3 Spoof Websites and Email Security Alerts 8.2.3.4 Virus Hoax Emails 8.2.3.5 Lottery Frauds 8.2.3.6 Pharming 8.2.3.7 Spoofing 8.2.3.8 Credit Card Fraud 8.2.3.9 Cyber Theft 8.2.3.10 Identity Theft 8.2.3.11 Theft of Internet Hours 8.2.3.12 Theft of Computer System (Hardware) 8.2.3.13 Cyber Terrorism 8.2.4 Flowing of Virus, Trojan Horse, Worm, and Logical Bombs 8.2.4.1 Cyber Pornography 8.2.4.2 Online Grooming 8.2.4.3 Sexting 8.2.4.4 Defamation 8.2.4.5 Cyberstalking 8.2.4.6 E-Mail and IRC-Related Crimes 8.2.4.7 Hacking and Cracking 8.2.5 Hackers 8.2.5.1 White Hat Hackers 8.2.5.2 Black Hat Hackers 8.2.5.3 Gray Hat Hackers 8.2.5.4 Red Hat Hackers 8.2.5.5 Blue Hat Hackers 8.2.5.6 Green Hat Hackers 8.2.5.7 Script Kiddies 8.2.5.8 Whistle-Blowers 8.2.5.9 Hacktivist 8.2.5.10 State/Nation-Sponsored Hackers 8.2.5.11 Cyber Delinquency 8.2.6 More Youth Involvement in Cybercrimes 8.2.7 Causes of Cybercrimes Among Youth 8.2.8 What Attracts Teenagers Toward Internet? 8.2.9 Reforms to Prevent Juvenile Cyber Deliquency 8.3 Cybercrime Against Minors 8.3.1 Steps to Combat 8.4 Case Studies 8.4.1 Miami-Dade Public School System Cyberattacks 8.4.2 Madrid Teen Hacker Allegedly Perpetrates “Numerous” Hacks Since Late 2019 8.5 Results and Discussion 8.6 Conclusion Notes 9 Deep Learning for Hate Speech and Offensive Language Detection 9.1 Introduction 9.2 Hate Speech and Offensive Language 9.2.1 Increasing Hate Speech and Offensive Language in Recent Times 9.2.2 Hate Speech as a Crime and Provisions in India 9.3 Detection Systems for HSOL 9.4 Deep Learning Detection Methods 9.4.1 Neural Network Architectures 9.4.2 Types of Deep Learning Methods 9.4.2.1 Word Embeddings-Based Methods 9.4.2.2 Transformer-Based Methods 9.4.3 Embeddings 9.4.3.1 Prediction-Based Embeddings (Word2vec) 9.4.3.2 Count-Based Embeddings (GloVe) 9.4.3.3 Bidirectional Encoder Representations From Transformers (BERT) 9.4.4 Classifiers 9.4.4.1 Naïve Bayes 9.4.4.2 Support Vector Machine (SVM) 9.4.4.3 Logistic Regression 9.4.4.4 Random Forest 9.4.4.5 Perceptron 9.4.5 Datasets Used 9.4.5.1 HASOC Dataset 9.4.5.2 Davidson HSOL Dataset 9.4.5.3 Founta Dataset 9.4.5.4 OLID Dataset 9.4.5.5 Sentiment140 Dataset 9.4.5.6 Gilbert Dataset 9.4.5.7 Tahmasbi and Rastegari Dataset 9.4.5.8 TREC 2020 (English) 9.4.5.9 Bohra Dataset 9.4.5.10 TREC 2020 (Hindi and Bangla) 9.5 Procedure of HSOL Detection 9.5.1 Convolutional Neural Networks (CNNs) 9.5.2 Recurrent Neural Networks (RNNs) 9.5.3 Gated Recurrent Unit (GRU) 9.5.4 CNN + GRU Combined Deep Neural Network 9.5.5 Long Short-Term Memory (LSTM) Model 9.5.6 Bi-Directional Long Short-Term Memory (Bi-LSTM) Model 9.5.7 Deep Convolutional Neural Network (DCNN) Model 9.6 Challenges Associated With Hate Speech Detection 9.6.1 Conflating Hate Speech With Offensive Language 9.6.2 Implicit Biases in Data-Driven Systems 9.6.3 Biases in Datasets and Embeddings 9.6.4 Availability and Inadequacy of Datasets 9.6.5 Rise of Adversarial Content 9.6.6 Potential Sources of Privacy Violations 9.6.7 Legal and Ideological Challenges 9.6.8 Code-Mixed Language 9.7 Conclusion Notes 10 Speech Processing and Analysis for Forensics and Cybercrime: A Systematic Review 10.1 Introduction 10.2 Generic Model of Speech Processing and Analysis for Forensics 10.2.1 Speaker Recognition 10.2.2 Speech Enhancement 10.2.3 Speech Synthesis 10.3 Challenges 10.4 Motivation 10.5 Various Applications 10.5.1 Cyber Security in Social Media 10.5.1.1 Social Media Cyber Security Measures That Work 10.5.2 Military 10.5.2.1 Network Forensics 10.5.2.2 Image Forensics 10.5.2.3 Audio and Speech Forensics 10.5.3 Law Enforcement 10.5.3.1 Network Forensics 10.5.3.2 Image Forensics 10.5.3.3 Audio and Speech Forensics 10.5.4 Business Domains 10.5.4.1 Network Forensics 10.5.4.2 Image Forensics 10.5.4.3 Audio and Speech Forensics 10.6 Review in Detail 10.6.1 Pre-Processing 10.6.2 Feature Extraction 10.6.3 Analysis 10.6.3.1 Speaker Recognition 10.6.3.2 Speech Enhancement 10.6.3.3 Speech Synthesis 10.7 Datasets 10.7.1 Speaker Recognition Datasets 10.7.2 Speech Enhancement Datasets 10.7.3 Speech Synthesis Datasets 10.8 Discussion and Future Directions 10.9 Conclusion References 11 Authentication Bypass Through Social Engineering 11.1 Introduction 11.1.1 Importance 11.2 Theoretical Background 11.2.1 Psychology of Social Engineering 11.2.2 Types of Social Engineering Attack 11.2.3 How to Prevent Social Engineering Attack Algorithm 11.3 Proposed Methodology 11.3.1 Download the HTML Index of the Target Webpage 11.3.2 Creating a PHP File for Password Harvesting 11.3.3 Modify the Page HTML File to Incorporate Your PHP File in It 11.3.4 Hosting the PHP File for Password Storing 11.3.5 Hosting the Actual Phishing Page 11.4 Experiment and Implementation 11.5 Results and Discussion 11.5.1 Stage 1: Planning Phase 11.5.2 Stage 2: Attack Preparation 11.5.3 Stage 3: Attack Conducting Phase 11.5.4 Stage 4: Valuable Acquisition Phase 11.6 Conclusion References 12 Emphasizing the Power of Natural Language Processing in Cyberbullying and Fake News Detection 12.1 Introduction 12.2 Related Works 12.3 Transformers With Attention Mechanism 12.4 Results and Discussion 12.5 Conclusion References Index