An understanding of how and why some stories go viral has been difficult for researchers to obtain. We can answer the question in part by using scale-free networks, block models, and other popular random graph models tested against their ability to explain cascade sizes, distributions of structural virality, and chaotic behavior seen in Mashable viral cascades.
We present the experiments we conducted, and explain their implications for the presence of sharing communities in our audience, the effects of the small-world characteristic, and other aspects of how networks contribute to virality. The result is our ability to answer fundamental questions for a media company: what clusters exist in our audience that we should be writing to? Who should we get our content in front of in order to see the best performance?
Lucio Tolentino is a computer and data scientist at Mashable, a digital tech-media company. Leveraging his background in computational epidemiology, he combs through data on how Mashable’s content is shared on social media for interesting and actionable insights. His research focuses on using network theory to understand news as a form of contagion, and applying machine learning to optimize content production.
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