Tracking the flu pandemic by monitoring the Social Web

We present a method for measuring the prevalence of disease in a population by analysing the contents of social networking tools, such as Twitter. Our method is based on the analysis of hundreds of thousands of tweets per day, searching for symptom-related statements, and turning statistical information into a flu-score that quantifies the diffusion of influenza-like illness (ILI) in various regions of the UK. This method uses completely independent data to that commonly used for these purposes, and can be applied at close time intervals, hence providing inexpensive and timely information about the state of an epidemic. Xv. Methodology Form a pool of candidate markers (features) from web pages related to influenza – we use an ency-clopedic reference from Wikipedia and a more infor-mal reference from the NHS website where potential flu patients discuss their personal experiences. We extract a set of K = 1560 stemmed candidate mark-ers denoted by MC = {mci}, i ∈ [1,K]. Compute their daily, regional, and unweighte