Guest Editors
Dr. Imran Ashraf, Yeungnam University, Korea.
Dr. Saru Kumari, Chaudhary Charan Singh University, India.
Dr. Rashid Ali, Universitat Pompeu Fabra, Barcelona, spain
Summary
Online Social Networks (OSNs), which are pretty popular and rapidly growing networks, are online platforms that focus on the social relationship, interaction, communication, and sharing of content & community-based inputs with friends, family members, and others. Approximately 3.6 billion active users were using OSNs in 2020, and it is estimated that a total of 4.41 billion users will be using the same globally by 2025. The users are reported to spend 144 minutes per day on average on different OSNs, which shows an increment of more than half an hour since 2015. As a result, a substantial amount of data is available on OSNs, and the same may be used to improve the quality of life of individuals and groups including manufacturers, service providers, advertisers, and AI researchers.
Artificial Intelligence (AI)-enabled solutions, including digital marketing of the products/ services, crowdsourcing to get new ideas from employees and customers, and recommending the most relevant tweets to the users, are prevalent nowadays. However, AI technologies also bring OSNs some challenges. For example, with the advancement of natural language processing, social robots have been able to use pre-trained multilingual models to generate human-like remarks on social platforms such as Twitter, which can be used to manipulate public opinion or incite emotions, and spread rumors or fake news. But, on the other hand, fake audio, video, text, and images generated by deep learning technologies can be spread across social networks.
This special issue provides a platform for researchers, academicians, and corporates to share emerging ideas on how AI can contribute to OSNs. Authors may submit their original work using various methodologies of AI in OSNs, including pattern recognition, intelligence web, the privacy of OSNs, ubiquitous computing, ambient intelligence, and big data.
Keywords
New mathematical models and architectures for AI-based OSNs
AI-enabled algorithm for detecting and tracking rumors
Intelligent pattern recognition for OSNs
Fake profile detection using AI
Machine learning-based algorithm for analysis of sentiments
AI-enabled techniques for gathering public opinion from OSNs
NLP-based market analysis and prediction
AI-enabled security and privacy for OSNs
Big data and data mining-based solutions, including analysis of multimedia traffic
Convolutional, recurrent neural networks and federated-based learning for OSNs
Transfer learning for OSNs
AI-enabled blockchain-based solutions for OSNs
AI-based data storage and data collection for large-scale OSNs
Intelligent system for optimization of OSNs.
AI for social Internet of Things
Online customer review
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