Advanced Technologies for Social Computing and Natural Language Processing

Dear Colleagues,

During recent decades, social media has revolutionized interpersonal communication and has consequently produced unprecedented amounts of user data, such as attitudes, opinions, interests, purchases, and activities, across numerous areas. Natural language processing (NLP) and social computing are promising and evolving fields for social media data analytics. With the increase in the volume and complexity of social media data, NLP and social computing are facing new challenges and opportunities. Recent advancements in technologies, such as artificial intelligence (AI), machine learning (ML), and large language models (LLMs), have led to significant improvements in these fields and have also brought concerns to social computing and NLP research. On the one hand, these advanced technologies offer unprecedented capabilities in managing multi-source and massive social data and conducting advanced data analytics, allowing researchers to gain a deeper understanding of complex social phenomena. On the other hand, ethical, privacy, and validity concerns are emerging regarding AI/ML, LLM, and social computing research.  In this Special Issue, both original research papers and reviews are welcome. Research may focus on (but is not limited to) the following topics:

• Privacy and trust in social computing and NLP;
• Ethical and legal issues in social computing and NLP;
• NLP and social computing for cyber threat intelligence;
• NLP for detecting information disorder on social media;
• Social computing and NLP in disaster management;
• Sentiment and opinion analysis on social media;
• Large language models and their applications in NLP and social computing;
• Multimodal NLP on social media;
• Text mining and information retrieval on social media;
• NLP and social computing for social eCommerce;
• Low-resource NLP for social computing\models of complex networks;
• Network embedding;
• Machine learning with graphs;
• Dynamics and evolution patterns of complex networks;
• Link prediction;
• Large-scale graph analytics;
• Social reputation, influence, and trust;
• Spread of information on social media;
• Rumour and viral marketing in social networks;
• Recommendation systems;
• Resilience and robustness of complex networks;
• Quantifying success through network analysis;
• Temporal and spatial networks;
• Graph-based natural language processing.

Dr. Sicong Shao
Dr. Diego Fregolent Mendes de Oliveira
Guest Editors

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• natural language processing
• artificial intelligence
• machine learning
• social computing