AUTHOR=Jiang Ke , Xu Qian 

TITLE=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

JOURNAL=Frontiers in Research Metrics and Analytics

VOLUME=Volume 8 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/research-metrics-and-analytics/articles/10.3389/frma.2023.1239726

DOI=10.3389/frma.2023.1239726

ISSN=2504-0537

ABSTRACT=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.