What question did this study set out to answer?

This work aims to explore how intelligent information is disseminated on social media platforms.

February 5, 2026

Dissemination intelligence information on social media

Key Points

This work aims to explore how intelligent information is disseminated on social media platforms.
Reviewed three key areas: rumor and fake news detection, competitive dissemination, and influential nodes identification.
Highlighted recent advancements in complexity-aware models and algorithms.
Examined the use of deep learning techniques for managing content credibility.
Identified the need for advanced models to improve information reliability.
Demonstrated the effectiveness of graph-based algorithms in discerning credible information.
Proposed future directions for bridging theory and practice in digital information ecosystems.

Abstract

Social media platforms, as complex socio-technical ecosystems, have reshaped the way individuals access, share, and interpret information. The interplay of diverse users, rapid content flow, and intricate network structures exemplifies the complexity science domain, where dynamic interactions give rise to emergent patterns of influence and opinion formation. In this perspective, we review three key areas of intelligent information dissemination on social media: 1) rumor and fake news detection, 2) competitive dissemination, and 3) the identification of influential nodes. We highlight recent advancements that leverage complexity-aware models, graph-based algorithms, and deep learning techniques to manage content credibility and guide the spread of verified information. Finally, we propose future research directions that bridge theory and practice, ultimately contributing to a more informed, reliable, and adaptive digital information ecosystem.

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