Dynamic wavelet fingerprint for differentiation of tweet storm types

• Published in: Social network analysis and mining Vol. 10; no. 1; p. 4
• Main Authors: Kirn, Spencer Lee, Hinders, Mark K.
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.12.2020; Springer Nature B.V
• Subjects: Applications of Graph Theory and Complex Networks; Automation; Clustering; Computer Science; Data mining; Data Mining and Knowledge Discovery; Datasets; Differentiation; Economics; False information; Fingerprinting; Fingerprints; Game Theory; Gossip; Humanities; Law; Methodology of the Social Sciences; News; Original Article; Probabilistic models; Social and Behav. Sciences; Social networks; Statistics for Social Sciences; Storms; Support vector machines; Time series; Topics; Transformation; Two dimensional analysis; Dynamic wavelet fingerprint technique; Twitter; Clustering; Dynamic topic modeling; Tweet storm
• ISSN: 1869-5450; 1869-5469
• DOI: 10.1007/s13278-019-0617-3

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.