Analyzing the dynamics of social media texts using coherency network analysis: a case study of the tweets with the co-hashtags of #BlackLivesMatter and #StopAsianHate - Jiang, Ke; Xu, Qian (Frontiers in Research Metrics and Analytics, 2023)
Coherency refers to the association between two time series, which can be measured using spectral analysis. The coherence squared, similar to the squared correlation coefficient, can be calculated to determine the extent to which changes in individual nodes are related and how they co-evolve. The resulting matrix of these relations can be analyzed using network analysis.Through a case study of the tweets using the co-hashtags of #StopAsianHate and #BlackLivesMatter, this paper proposes a novel approach to use coherency network analysis to research the dynamics of social media text. By using frequency domain analysis or spectral analysis, the coherence squared is calculated to illustrate the relationship and co-evolution of individual nodes. Additionally, the slope of the phase spectrum is analyzed to determine the time lag and potential direction of causality between highly co-evolved node pairs.