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
Ke Jiang1*
Qian Xu1
- 1Elon University, United States
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.
Keywords: Coherance network anlaysis, semantic network, Social Media Text Analysis, Hashtag Activism, Tweet analysis
Received: 13 Jun 2023;
Accepted: 25 Sep 2023.
Copyright: © 2023 Jiang and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Ke Jiang, Elon University, Elon, United States