User Behavior Prediction on Social Media Using Artificial Intelligence
A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications".
Deadline for manuscript submissions: 17 February 2027 | Viewed by 1
Special Issue Editor
Interests: social media analysis; natural language processing; machine learning; deep learning; agent-based modeling; semantic web
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Social media platforms have become a central component of modern digital ecosystems, generating vast volumes of heterogeneous data that reflect users’ preferences, interactions, and behavioral patterns. Understanding and predicting user behavior in such environments can be important for a wide range of applications, including information diffusion analysis, personalized recommendations, marketing strategies, monitoring of public opinion, and decision support systems. In this context, artificial intelligence (AI) has emerged as a powerful paradigm for extracting meaningful insights from complex, large-scale social media data.
The primary focus of this Special Issue is on user behavior prediction on social media using artificial intelligence, with particular emphasis on data-driven, intelligent, and scalable computational approaches. The scope of the Special Issue encompasses a broad range of AI methodologies, including machine learning, deep learning, natural language processing, graph-based models, and hybrid intelligent systems, as applied to the modeling, analysis, and prediction of user behavior on social media platforms. Both methodological contributions and application-oriented studies are welcome, as are interdisciplinary works that integrate perspectives from computer science, data science, social sciences, business, and economics.
The purpose of this Special Issue is threefold. First, it aims to provide a consolidated venue for recent advances in AI-based approaches to user behavior prediction, highlighting state-of-the-art techniques and emerging trends. Second, it seeks to promote methodological innovation by encouraging contributions that address challenges such as data heterogeneity, dynamic user behavior, explainability, scalability, and ethical considerations. Third, the issue intends to foster interdisciplinary dialog by showcasing how AI-driven behavioral prediction can inform practical decision-making and theoretical understanding across multiple domains.
Prof. Dr. Liviu-Adrian Cotfas
Guest Editor
Manuscript Submission Information
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Keywords
- user behavior prediction
- behavioral analytics
- social media analysis
- artificial intelligence
- natural language processing
- machine learning
- deep learning
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