Big social data (BSD) analytics and visualization are the most promising computing platforms in artificial intelligence, information mining, and statistics that use statistical pattern recognition, collaborative learning, database management, visualization technology, and social networking together. The BSD has numerous issues over machine learning and information fusion to data mining and web of things (WoT) computing in social networks and the semantic web towards Industry 4.0 and cyber-physical social systems. The BSD continuously gathers digital social data in high-velocity, high-volume, and high-variety multimedia databases for social media interactions. The BSD datasets have massive user information. The process of BSD usually consists of three stages: (a) data acquisition; (2) data processing (preprocessing, data mining, etc.); and (3) data validation and visualization. The internet of things (IoT) and web technologies link objects daily and facilitate machine-to-machine communications through physical-to-virtual world interactions.
An intelligent social data analytics tool improves business decision support by extracting meaningful user information from social sites like Facebook, Twitter, Amazon, Linkedin, and Google. Here, the information comes from various sources and accumulates traffic, health, weather, transportation data, and posts on various social media sites. Social sites mainly handle/share multimedia text, audio, video, and data services through distributed, semantic and social computing. The main issues focus on data processing, storage, pattern mining, manipulation, user behavior analysis, privacy and security (including cyberbullying), and high-performance real-time communications. Artificial intelligence/computational intelligence can enhance proper and faster decision capabilities via BSD.
This special issue focuses on the recent development of big social data analytics methods for improving quality of life (QoL). Thus, we here seek the most impactful and newest findings on processing and visualizing the BSD using emerging artificial intelligence/computational intelligence in WoT and social multimedia networks. As follows, a brief list of some desired directions is mentioned.
Topic areas include but are not limited to, the following:
- Collaborative learning-assisted BSD management over WoT
- Cyber-physical systems for BSD
- Cyber-bullying in WoT and social networks
- Transportation and traffic information processing in future smart cities through social networks AI-enabled BSD service management
- Cloud/fog/edge-based task management and offloading for WoT
- Robust optimization techniques for BSD analytics
- Computational intelligence in remote BSD analytics
- Data ethics and privacy in social IoT/WoT
- Complex social data analysis: HOW TO MANAGE?
- Advanced communication systems for WoT-BSD platforms
- Semantic web and sentiment analysis for BSD
- Multimedia computing for BSD analytics
- Decision support systems and ontology for BSD
Submission Guidelines
For author information and guidelines on submission criteria, please visit the TBD’s Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.
Questions?
Contact the guest editors:
- Chinmay Chakraborty, Birla Institute of Technology, Mesra, India
- Houbing Song, University of Maryland, Baltimore, USA
- Guangjie Han, Hohai University, China
- Suman Ghosh, Finland Research Centre Huawei, Finland
