Dynamic wavelet fingerprint for differentiation of tweet storm types

Abstract

We describe a novel method for analyzing topics extracted from Twitter by utilizing the dynamic wavelet fingerprint technique (DWFT). Topics are derived from 7 different tweet storms analyzed in the study by using a dynamic topic model. Using the time series of each topic, we run DWFT analyses to get a two-dimensional, time-scale, binary image. Gaussian mixture model clustering is used to identify individual objects, or storm cells, that are characteristic to specific local behaviors commonly occurring in topics. The DWFT time series transformation is volume agnostic, meaning we can compare tweet storms of different intensities. We find that we can identify behavior, localized in time, that is characteristic to how different topics propagate through Twitter. The use of dynamic topic models and the DWFT create the basis for future applications as a real-time Twitter analysis system for flagging fake news.