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Center for Nonlinear Studies |
The proliferation of social media (Twitter, Facebook, blogs, etc.) has created an unprecedented, continuous stream of messages containing the thoughts of millions of people. The nascent field of Social Media Analysis (SMA) combines natural language processing, data mining, machine learning, and statistics to explore what we can infer from the behavior of social media users. Recent research suggests that such analysis can provide insights into public health, finance, politics, social unrest, and natural disasters. In this sense, SMA can be understood as an alternative to slower and more costly data collection methods, such as surveys and opinion polls. In this talk I will first give an overview of SMA methodology, then present results from three recent applications: (1) estimating national influenza rates, (2) estimating alcohol consumption volume, (3) assessing personal risk perception prior to an impeding natural disaster. These results suggest that relatively simple methods can extract socially valuable insights from this rich source of data. I will conclude with a discussion of open problems and discuss how more sophisticated machine learning algorithms (graphical models, semi-supervised learning) may expand the capabilities of this emerging field of study.
Host: Reid Priedhorsky