Combating Fake News with Computational Intelligence Techniques (Studies in Computational Intelligence, 1001)

✍ Scribed by Mohamed Lahby (editor), Al-Sakib Khan Pathan (editor), Yassine Maleh (editor), Wael Mohamed Shaher Yafooz (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 432
• Category: Library

✦ Synopsis

This book presents the latest cutting-edge research, theoretical methods, and novel applications in the field of computational intelligence techniques and methods for combating fake news.Fake news is everywhere. Despite the efforts of major social network players such as Facebook and Twitter to fight disinformation, miracle cures and conspiracy theories continue to rain down on the net. Artificial intelligence can be a bulwark against the diversity of fake news on the Internet and social networks.

This book discusses new models, practical solutions, and technological advances related to detecting and analyzing fake news based on computational intelligence models and techniques, to help decision-makers, managers, professionals, and researchers design new paradigms considering the unique opportunities associated with computational intelligence techniques. Further, the book helps readers understand computational intelligence techniques combating fake news in a systematic and straightforward way.

✦ Table of Contents

Preface
Contents
About the Editors
State-of-the-Art
Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study
1 Introduction
2 Related Work
3 Research Methodology
3.1 Defining Research Questions
3.2 Formulating Search Strings
3.3 Selecting Papers from Digital Databases
3.4 Data Extraction Strategy
3.5 Analysis and Classification
4 Results and Discussion
4.1 Overview of the Selected Studies
4.2 RQ1: In Which Years, Sources, and Publication Channels Papers Were Published?
4.3 RQ2: Which Research Types Are Adopted in Selected Papers?
4.4 RQ3: Which Domain Fields Are Targeted in Selected Papers?
4.5 RQ4: Which Social Media Platforms Are the Major Source of Fake News to the End Users?
4.6 RQ5: Which Contexts Are Targeted in Selected Papers?
4.7 RQ6: What Kinds of Fake News Are Targeted in Selected Papers?
4.8 RQ7: Which Types of Features Are Exploited for Fake News Detection?
4.9 RQ8: Which Machine Learning Models, Data Mining Tasks, and Techniques Are Used to Deal with Fake News?
5 Implications for Researchers
5.1 RQ1
5.2 RQ2 and RQ3
5.3 RQ4
5.4 RQ5
5.5 RQ6 and RQ7
5.6 RQ8
6 Conclusion
References
Using Artificial Intelligence Against the Phenomenon of Fake News: A Systematic Literature Review
1 Introduction
2 Related Work
3 Methodology
3.1 Research Questions
3.2 Search Strategy
4 Results and Discussion
5 Conclusion
6 Declarations
6.1 Competing Interests
6.2 Funding
References
Fake News Detection in Internet Using Deep Learning: A Review
1 Introduction
2 Methodology
3 Fake News: Why Are They Used?
4 Deep Learning and Fake News
4.1 Fake News Detection
4.2 Deep Learning Applications
5 Deep Learning Algorithms
6 Current Research
7 Future Research
8 Discussion
9 Conclusion
References
Machine Learning Techniques and Fake News
Early Detection of Fake News from Social Media Networks Using Computational Intelligence Approaches
1 Introduction
2 Related Works
3 Research Methods
3.1 Data Collection and Variable Definition
3.2 Model Design
3.3 Genetic Algorithm
3.4 K-Nearest Neighbor (KNN)
3.5 Ensemble Learning
3.6 Evaluation Measures
4 Results and Analysis
4.1 Discussion
5 Conclusion and Future Works
References
Fandet Semantic Model: An OWL Ontology for Context-Based Fake News Detection on Social Media
1 Introduction
2 Related Work
2.1 State-Of-The-Art with Semantic Technologies
3 Taxonomy for Context-Based Fake News Detection
4 Fandet Ontology
4.1 Fandet Ontology Classes
4.2 Fandet Ontology Object Properties
4.3 Fandet Ontology Data Properties
5 Fandet Ontology Use Case
6 Conclusion
References
Fake News Detection Using Machine Learning and Natural Language Processing
1 Objective
2 Introduction
3 Previous Approaches
4 Design Tools and Technologies
4.1 Machine Learning
4.2 Deep Learning
4.3 Recurrent Neural Network
4.4 Natural Language Processing
5 Methodology
5.1 Web Scraping and Dataset Development
5.2 Proposed Work
6 Results and Discussion
7 Conclusion
References
Fake News Detection Using Ensemble Learning and Machine Learning Algorithms
1 Introduction
2 Materials and Methods
2.1 Datasets
2.2 Models Design
2.3 Classification Algorithms
2.4 Evaluation Metrics
3 Results and Discussion
4 Conclusion
References
Evaluation of Machine Learning Methods for Fake News Detection
1 Introduction
1.1 Motivations and Contributions
2 Related Work
3 Investigated Algorithms
3.1 L1 Regularized Logistic Regression
3.2 C-Support Vector Classification
3.3 Gaussian and Multinomial Naive Bayes
3.4 Decision Trees
3.5 Random Forests
3.6 Multi-Layer Perceptron (MLP)
3.7 Convolutional Neural Networks (CNNs)
4 Evaluation Environment and Settings
4.1 Competing Algorithms
4.2 Execution Environment
4.3 Datasets
4.4 Performance Measures
5 Performance Evaluation
5.1 Text-To-Vector Transformation
5.2 How Many Dimensions Are Necessary?
5.3 Method of Choice to Generate Vector Representations
5.4 Execution Time
5.5 Summary of findings
6 Future Directions
7 Conclusions
Referesnces
Credibility and Reliability News Evaluation Based on Artificial Intelligent Service with Feature Segmentation Searching and Dynamic Clustering
1 Introduction
2 Preparation of Your Paper
3 Additional Information Required from Authors
3.1 Web Crawler Procedure
3.2 Text Segmentation and Extension Method
3.3 Classification and Clustering Procedure
3.4 Fake New Prediction and Warning Procedure
4 Verification and Results
5 Discussion
6 Conclusion
References
Deep Learning with Self-Attention Mechanism for Fake News Detection
1 Introduction
2 Related Work
3 Theoretical Framework
3.1 Word Embeddings
3.2 Self-Attention Mechanism
3.3 Sliding Window Self-Attention
3.4 Bidirectional Encoder Representations from Transformers—BERT
4 Experimental Setup and Results
4.1 Dataset Collection
4.2 Models Design and Implementation
5 Conclusion
References
Modeling and Solving the Fake News Detection Scheduling Problem
1 Introduction
2 Modeling of the FND Problem with FSSP
3 Resolution Approach
3.1 The IG Algorithm
3.2 The ABC Algorithm
3.3 The GA Algorithm
4 Experimental Result
5 Conclusion
References
Case Studies and Frameworks
The Multiplier Effect on the Dissemination of False Speeches on Social Networks: Experiment during the Silly Season in Spain
1 Introduction and State of the Question
2 Material and Methods
3 Analysis and Results
4 Discussion and Conclusions
References
Detecting News Influence in a Country: One Step Forward Towards Understanding Fake News
1 Introduction
2 Related Work
3 Our Results
4 Preliminaries
5 Overview of the Application
6 Technical Details
6.1 Web Crawling
6.2 Text Comparison
6.3 Technologies Used
7 Conclusions and Future Work
References
Factors Affecting the Intention of Using Fintech Services in the Context of Combating of Fake News
1 Introduction
2 Literature Review
3 Research Models
3.1 Trust Scale (Symbol TN)
3.2 Perceived Usefulness Scale (Symbol HI)
3.3 Easy-To-Use Cognitive Scale (Symbol DSD)
3.4 Scale of Social Influence (Symbol XH)
3.5 Innovation Scale (Symbol TM)
3.6 Fintech Service Communication Scale (Symbol DV)
3.7 Scale of Intention to Accept and Use Services (Symbol YD)
4 Research Methodology and Data
5 Research Results
5.1 Descriptive Statistics
5.2 EFA—Exploratory Factor Analysis
5.3 Regression Results
6 Conclusions
References
Crowd Sourcing and Blockchain-Based Incentive Mechanism to Combat Fake News
1 Introduction
2 Related Work
3 Preliminaries
3.1 Why Blockchain?
3.2 How Does the Blockchain Make Its Decisions?
3.3 Smart Contract
4 Our Model
4.1 Proposed Framework
4.2 System Architecture
4.3 Consensus Rule
4.4 Reward Generation Mechanism
5 Prototype Implementation
5.1 Implementataion and Basic Components of dApp
5.2 Overall Working of dApp
5.3 Test Implementation
6 Results and Discussion
6.1 Performance Evaluation
6.2 Defense Against Attacks
7 Relevance of Our Proposed Model in Current World
8 Conclusion and Future Work
References
Framework for Fake News Classification Using Vectorization and Machine Learning
1 Introduction
2 Materials and Methods
2.1 Natural Language Processing (NLP)
2.2 Machine Learning Algorithm
3 Evaluation Metrics
3.1 Confusion Matrix
3.2 Jaccard Index
3.3 Kappa Index
3.4 Log-Loss
4 Results and Discussion
5 Conclusion
References
Fact Checking: An Automatic End to End Fact Checking System
1 Introduction
2 Literature Review
3 Methodology
3.1 Data Exploration
3.2 Model Description
4 Web Application Development Task
4.1 Source Collection
4.2 Text Retrieval
4.3 Aggregation
5 Evaluation Results
5.1 Fake Claim Example
5.2 Non Fake Claim Example
5.3 Unverified Claim Example
6 Conclusion and Future Directions
References
Fake News and COVID-19 Pandemic
False Information in a Post Covid-19 World
1 Introduction
1.1 Chapter Organization
2 Fake Test Results
3 False Vaccination
4 Covid-19 Propaganda
5 False Information in Social Media
5.1 Evidence of Misinformation in Social Media
6 False Information in Media Forms Outside of Social Media
6.1 Print Media
6.2 Broadcast Media
7 Conclusion
References
Applying Fuzzy Logic and Neural Network in Sentiment Analysis for Fake News Detection: Case of Covid-19
1 Introduction
2 Related Work
3 Main Concepts
3.1 Sentiment Analysis
3.2 Fuzzy Logic
3.3 Long-Shirt-Term-Memory (LSTM)
4 Proposed Approach
4.1 Dataset Description
4.2 Dataset Statistics
4.3 Proposed Approach
5 Results and Discussions
5.1 Results
6 Conclusion and Future Work
References
Analyzing Deep Learning Optimizers for COVID-19 Fake News Detection
1 Introduction
2 Related Work
3 Background
3.1 Deep Learning Optimizers
3.2 CoCoB Optimizer
3.3 CoCoB Optimizer
4 Methodology Overview
4.1 Data Collection and Cleaning
4.2 Generating Bag of Words Models
4.3 Building and Training Model
5 Experimental Setup
5.1 About the Dataset
5.2 Deep Learning Models
5.3 Optimizers and Parameters
5.4 System Configuration
6 Result Analysis and Discussion
6.1 Exploratory Data Analysis
6.2 Deep Learning Optimizer Analysis
7 Conclusion and Future Direction
References
Detecting Fake News on COVID-19 Vaccine from YouTube Videos Using Advanced Machine Learning Approaches
1 Introduction
2 Related Studies
2.1 Content-Based Approach
2.2 Context-Based Approach
2.3 Propagation Approach
3 Methods and Materials
3.1 Data Acquisition Phase
3.2 Annotation Process Phase
3.3 Classification Methods
3.4 Results Evaluation
4 Results Discussion
5 Conclusion
References