Abstract
Online social media has emerged as a critical factor in information dissemination, search, marketing, expertise and influence discovery, and potentially an important tool for mobilizing people. It has also given researchers access to massive quantities of social data for empirical analysis. These data sets offer a rich source of evidence for studying dynamics of individual and group behavior, the structure of networks and global patterns of the flow of information on them. However, in most previous studies, the structure of the underlying networks was not directly visible but had to be inferred from the flow of information from one individual to another. As a result, we do not yet understand dynamics of information spread on networks or how the structure of the network affects it. We analyze data from two popular social news sites, Digg and Twitter, to understand the mechanisms of information diffusion in social networks, in the process, uncovering the primary role played by individual’s cognitive constraints in online social behavior.
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Notes
- 1.
Note that even above epidemic threshold, cascades that start in an isolated region of the graph will die out.
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Lerman, K. (2013). Social Informatics: Using Big Data to Understand Social Behavior. In: Michelucci, P. (eds) Handbook of Human Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8806-4_60
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DOI: https://doi.org/10.1007/978-1-4614-8806-4_60
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