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Branching process descriptions of information cascades on twitter
Date
2021
Abstract
A detailed analysis of Twitter-based information cascades is performed, and it is demonstrated that branching process hypotheses are approximately satisfied. Using a branching process framework, models of agent-to-agent transmission are compared to conclude that a limited attention model better reproduces the relevant characteristics of the data than the more common independent cascade model. Existing and new analytical results for branching processes are shown to match well to the important statistical characteristics of the empirical information cascades, thus demonstrating the power of branching process descriptions for understanding social information spreading.
Supervisor
Description
peer-reviewed
Publisher
Oxford University Press
Citation
Journal of Complex Networks; 8 (6)
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ULRR Identifiers
Funding code
Funding Information
Science Foundation Ireland (SFI), European Union (EU)