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Showing posts with the label trump

Using math to extract information from social data

Many people say we are in the information era, but it seems that we are passed this. Nowadays, information is within everyone's reach, about everything and as much as we want. Data is not the issue anymore, at least most of the time. The real issue is  how to analyze the data.  It seems that having information is not the problem now, but actually having too much  data. One of the places in which we can find too much data are social networks. The richness of social networks is that they are a continuous flow of interesting data, what I like to call social data .  Social data is so rich as you can extract information from it in so many ways. One is to analyze what people express over a specific topic on social media. To this end, I developed  a way to identify the most important ideas found on a stream of user comments. Basically an algorithmic summary tool.  With a data set of a few tens of user comments, it is easy to grasp the general feelings and thoughts that

2016 General Election Prediction

The presidential election is just few days away and this is the perfect time to jump into the prediction game as many others had ( Nate Silver , Sam Wang ,  Drew Linzer , and others). One way to estimate the outcome of the election results is to use data from polls. Many factors can be taken into consideration when performing a projection, such as historical trends, media coverage, debates, etc., but the advantage of considering polls is that it gives a data that is chronologically close to the desired event. We can think about the election as a guessing game: How much does each candidate measure  on election day? We are measuring  each candidate on the population on election day and polls give a good source of information on how this measurement is currently changing. It is important to realize that these measurements are dynamic  and get affected by events.  Every time big polls are published, knowing the current measurement makes it to change . This is known in physical