SCOPE

The recent fast growth and evolution of wireless technologies such as 3G, 4G and 5G, have shifted the behaviour of users concerning the utilization of the Internet. At the same time, the development and deployment of several streaming servers have given the users the privilege to use different multimedia applications. Among these services, social networks such as Facebook, YouTube, Twitter, etc, have a good standing and they represent the most used applications by the people, everywhere and for all times so far. Moreover, the traffic generated by digital media platforms (social networks and digital newspapers) represent more than half of the web traffic worldwide.

Furthermore, the use of these digital media platforms has greatly influenced our habits on the Internet and in our daily life. As a result, we must radically be wary of the contents posted and shared via social networks. Indeed, with widespread dissemination of information via digital media platforms, users are required to judge the credibility of the information by eliminating fake news. This phenomenon is becoming a worldwide issue, it is considered as one of the greatest threats to many disciplines such as democracy, journalism, economics, healthcare, etc.

On the other hand, Computational Intelligence (CI) techniques have shown promise to be powerful tools in various domains, such as computer vision, natural language processing (NLP), speech recognition, computational biology, and others. Motivated by these successes, researchers all over the world have recently started investigating applications of these techniques to handle fake news issue in the modern media.

In this context, in the recent time, many methods such as Fuzzy Logic (FL), Evolutionary Algorithms (EA), Machine Learning (ML), Artificial Neural Networks (ANN) and Deep Learning (DL) have been developed to deal with widespread dissemination of information generated by digital media platforms. This book examines how computational intelligence techniques have contributed to the battle against Fake news.

TOPICS

Fake news: State-of-the-Art Survey

• Overview of Machine Learning Techniques

• Overview Deep Learning Techniques

• Overview on Fake News Features Detection/Prevention

• Fake News in Online Unstructured Data

• Fake News sources and Social Media

Artificial Intelligence for fake news detection

• Machine Learning and Ensemble Learning Approaches

• Deep Learning Models

• Text-Mining and Sentiment Analysis Techniques

• Graph Optimization Algorithms

• Explainable Artificial Intelligence

• Fake News Detection Techniques and Coronavirus Epidemic.

Fake news prevention techniques

• Machine Learning and Ensemble Learning

• Deep Learning

• Text-Mining and Sentiment Analysis Techniques

• Graph Optimization Algorithms

• Explainable Artificial Intelligence

• Fake News Prevention Techniques and Coronavirus Epidemic.

Feature engineering on Fake News

• Linguistics, Writing Style and Sentiment Based Features

• Social-Context Based and User’ Information Based Features

• Feature Selection, Extraction, Construction and Reduction

• Feature Analysis on Fake News Detection/Prevention

Solutions and frameworks in the practice

• Benchmark Dataset for Fake News Detection/Prevention

• Real-World System for Fake News Detection/Prevention

• BlockChain Technology on Fake News

• Tracking and aggregating Online Fake News

• Fake News and Social Media

• New Trends on News