AI and Blockchain for Trustworthy Social Computing

Dear Colleagues,

Social media platforms have become the primary channels for information dissemination and public discourse. However, current centralized social networks face severe challenges, including the proliferation of misinformation (fake news), privacy breaches, algorithmic bias, and centralized censorship. This Special Issue explores the convergence of Artificial Intelligence (AI) and Blockchain to reconstruct trust in social computing. We focus on how Blockchain can provide a decentralized, immutable infrastructure for content provenance and user sovereignty, while AI enables intelligent content analysis, precise information diffusion modeling, and robust anomaly detection. The scope extends to Decentralized Social Networks (DeSoc), cross-platform information propagation, and privacy-preserving social graph analysis. By integrating these technologies, this issue aims to chart the path toward a more transparent, secure, and user-centric social ecosystem.

(1) In the digital age, social networks are the nervous system of modern society. While they connect billions, the erosion of trust in these platforms is undeniable. The spread of rumors and deepfakes undermines public consensus, while the centralization of user data raises significant privacy concerns. Traditional solutions often rely on central authorities for moderation, which lacks transparency and resilience. Blockchain and Artificial Intelligence offer a dual-engine solution to these systemic issues. Blockchain technology introduces the concept of "Code is Law" to social interactions, enabling decentralized identity management, immutable content tracing, and tamper-proof reputation systems. Concurrently, AI advances in Graph Neural Networks (GNNs) and Natural Language Processing (NLP) provide powerful tools for modeling information dynamics, detecting malicious bots, and understanding social contagion patterns. The fusion of these fields—Trustworthy Social Computing—is essential for building the next generation of social platforms (Web 3.0 Social).

(2) This Special Issue aims to bring together researchers from the fields of social computing, network security, and distributed systems to address the challenges of trust in social networks. We seek contributions that propose innovative frameworks where AI enhances the intelligence of social analysis and Blockchain guarantees the integrity of social data. This topic strongly aligns with BlockSys'2026’s scope by applying "Trustworthy Systems" principles to the domain of human interaction and data dissemination. It highlights the non-financial, societal value of blockchain technology, emphasizing its role in governing information flow and protecting digital human rights.

(3) Suggest themes.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Information Diffusion Modeling: AI-driven prediction of information cascades, rumor propagation, and cross-platform trend analysis.
• Misinformation and Bot Detection: Leveraging graph mining and blockchain provenance to identify fake news, deepfakes, and social bots (Sybil attacks).
• Decentralized Social Networks (DeSoc): Architectures, protocols, and incentive mechanisms for blockchain-based social media.
• Privacy-Preserving Social Computing: Federated learning for recommendation systems and social graph analysis without compromising user data.
• Digital Identity (DID) and Reputation: Blockchain-based identity management and trust scoring systems in social communities.
• Social Graph Analysis: Dynamic graph neural networks for link prediction, community detection, and influence maximization in decentralized graphs.
• Governance in Social DAOs: AI-assisted decision-making and voting mechanisms for online communities. 

We look forward to receiving your contributions. 

Prof. Dr. Yin Zhang
Prof. Dr. Tianqing Zhu
Dr. Ranran Wang
Guest Editors

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• social computing
• information cascade prediction
• decentralized social networks (DeSoc)
• misinformation detection
• content provenance tracking
• dynamic graph learning