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Natchanon Suaysom

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Iterative Matrix Factorization Method for Social Media Data Location Prediction

Arthur T. Benjamin
Second Reader(s)
Puck Rombach (UCLA)


Since some of the location of where the users posted their tweets collected by social media company have varied accuracy, and some are missing. We want to use those tweets with highest accuracy to help fill in the data of those tweets with incomplete information. To test our algorithm, we used the sets of social media data from a city, we separated them into training sets, where we know all the information, and the testing sets, where we intentionally pretend to not know the location. One prediction method that was used in (Dukler et al. 2016) requires appending one-hot encoding of the location to the bag of words matrix to do Location Oriented Nonnegative Matrix Factorization (LONMF). We improve further on this algorithm by introducing iterative LONMF. We found that when the threshold and number of iterations are chosen correctly, we can predict tweets location with higher accuracy than using LONMF.

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