Networked Social Influence and Acceptance in a New Age of Crises
Abstract
In this project, we set out the ambition to create a modeling framework, grounded in real and diverse online social network data, on how to combine complex social network analysis with nonlinear human behaviour dynamics. This has the advantage of being able to uncover hidden influencers. The objectives are:1.Who are the real influencers: finding influencers on social networks as a function of network topology (graph algebra) and nonlinear influence behaviour dynamics (differential equations, data embeddings, etc.). 2 Explainability: identify what parameters cause influencers to be influential: e.g. network place (eigenvalue) and personal behaviour dynamics. Analytical explainability also provides a pathway towards understanding how to sparsely monitor vast networks, e.g., which key influencers should be monitored and why. This has widespread applications in counterterrorism, social media analysis, and understanding how we can effectively spread policies and create acceptance to technologies in an age of emerging crises.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 09, 2024
- Accession Number
- AD1228875
Entities
People
- Weisi Guo
Organizations
- Cranfield University