- Submission Deadline: 30 June, 2023
- Publication Date: March 2024 (Tentative)
Nowadays, online platforms play a growing part in our daily lives. A variety of platforms have been used to spread information, including blogs, forums, online chats, social media (Facebook, Twitter, Instagram), discussion websites (Reddit), messaging applications (WhatsApp, Snapchat), and social media in general. Without a doubt, social media platforms like Facebook, Twitter, and Instagram benefit society since they allow users to express themselves and seek support from others in the online community. These platforms also have a negative effect on our society. Additionally, these social media platforms offer a venue for bad actors to engage in antisocial behaviours such as trolling, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news, spam, and fraud. On the other hand, people also use these social media platforms where people or victims communicate directly with officials during a disaster to ask for help. Officials also use these platforms to know the floor reality of the disaster and provide rescue and relief operations efficiently. Although several models exist to address these issues. Linguistic differences and the availability of social media data in various modalities (such as text, image, video, and audio) necessitate the development of new advanced and robust Natural Language Technique (NLP), Machine Learning (ML), and Deep Learning (DL) techniques. These techniques can give a set of powerful tools for extracting hidden information and related knowledge from social media posts in various linguistics and modalities for efficient fake news, rumour, hate speech, fraud, and spam identification.
This special section aims to go through the advanced learning intelligence strategy that will aid in developing a smart society. This special section intends to collect quality and ongoing research articles on this current topic. The special section addresses the current taxonomy with the help of advanced learning intelligence. We encouraged academic and industry researchers, developers, and practitioners to submit original research articles describing theoretical and experimental findings.
The topics of this special section include, but are not limited to:
- Social media analytics for disaster management
- Fake news and rumour detection on social media
- Hate and offensive language identification on online social media
- Computational Intelligence for cyberbullying detection on social media
- Sarcasm detection in social media posts based on learning intelligence
- Social media fraud detection based on learning intelligence
- Intelligent system for social media profile spoofing
- Intelligent system for increasing brand popularity on social media
- Social media text summarization based on learning intelligence
- Social media multi-modal feature fusion based on deep learning
- Computation intelligence for spam detection on social media
- Artificial Intelligence technique for Adverse drug reaction (ADR) detection
- Computational Intelligence for online pornography detection
For author information and submission criteria for full papers, please visit the Author Information page. Please submit full papers through the ScholarOne system.
The submitted papers must be written in English and describe original research not published nor currently under review by other journals or conferences. Submissions should include an abstract, 5-10 keywords, and the e-mail address of the corresponding author. Parallel submissions will not be accepted. All submitted papers relevant to the theme and objectives of the special issue will go through an external peer-review process.
Questions? Contact the guest editors.