Volume 18 • Issue 2 • PP: 239-257 • 2026
Exploring the Relationship between Social Network Structures and Emotional Contagion using NLP and Network Science
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Natural Language Processing (NLP) and Network Science were combined to study emotional contagion dynamics in social media networks. We simulated the diffusion of emotions through users on a synthetic interaction network using sentiment-labeled Twitter data and a graph-based model. We explored the relationship between graph metrics, including centrality and clustering coefficient, on emotion propagation and stability. The findings show that emotion intensity converges through the network and that both weak coupling of central nodes and moderate cluster structures dampen the spread of emotion. A community-level analysis reveals more alternative results, such as the fact that emotions differ in polarity between communities. Our work demonstrates a better understanding of how emotional behavior in online environments can be adjusted using semantic measures, which offer a means to characterize the relevance of information online and the interconnected relationships among emotionality.
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References
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