Information flows in online social networks

Dr. Lewis Mitchell (School of Mathematical Sciences, University of Adelaide)
Title: Information flows in online social networks
Abstract: The flow of information online is a significant factor in social contagion, rumour and “fake news” propagation, and protest organisation. Further, online social platforms provide a unique opportunity for computational social scientists to observe individuals’ spontaneous interactions over social ties, often through structural or temporal proxies for information. However, such approaches do not leverage the full extent of information available, namely the time-ordered textual content of messages. Here we apply information-theoretic techniques to social media data to identify the extent to which predictive information is encoded in social ties, and that in principle one can profile an individual from their contacts even if the individual is ``hidden'' within the network. Analysis of Twitter users shows that 95% of the potential predictive accuracy attainable for an individual is embedded within their social ties, and numerical simulations on a paradigmatic model of information flow shows that these techniques are robust.

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