Combating Fake News with Computational Intelligence Techniques

Part I: State-of-the-art.- Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study.- Using Artificial Intelligence against the Phenomenon of Fake News: a Systematic Literature Review.- Fake news detection in internet using deep learning: A review.- Part II: Machine Learning Techniques and Fake News.- Early Detection of Fake News from Social Media Networks using Computational Intelligence Approaches.- Fandet Semantic Model: An OWL Ontology for Context-Based Fake News Detection on Social Media.- Fake News Detection using Machine Learning and Natural Language Processing.- Fake News Detection using Ensemble Learning and Machine Learning Algorithms.- Evaluation of Machine Learning Methods for Fake News Detection.- Credibility and Reliability News Evaluation Based on Artificial Intelligent Service with Feature Segmentation Searching and Dynamic Clustering.- Deep Learning with Self-Attention Mechanism for Fake News Detection.- Modeling and solving the fake news detection scheduling problem.- Part III: Case Studies and Frameworks.- The multiplier effect on the dissemination of false speeches on social networks: Experiment during the silly season in Spain.- Detecting News Influence in a Country: One Step Forward Towards Understanding Fake News.- Factors Affecting the Intention of Using Fintech Services in the Context of Combating of Fake News.- Crowd Sourcing and Blockchain-based Incentive Mechanism to Combat Fake News.- Framework for Fake News Classification using Vectorization and Machine Learning.- Fact Checking: An Automatic end to end Fact Checking System.- Part IV: Fake news and Covid-19 pandemic.- False Information in a Post Covid-19 World.- Applying Fuzzy Logic and Neural Network in Sentiment Analysis for fake news detection: Case of Covid-19.- Analyzing Deep Learning Optimizers for COVID-19 Fake News Detection.- Detecting Fake News On COVID-19 Vaccine from YouTube Videos Using Advanced Machine Learning Approaches.