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ORIGINAL RESEARCH article

Front. Res. Metr. Anal.
Sec. Research Methods
Volume 9 - 2024 | doi: 10.3389/frma.2024.1417990
This article is part of the Research Topic Network Analysis of Social Media Texts View all 4 articles

The Multilayer Semantic Network Structure of Community Tensions

Provisionally accepted
  • School of Communication and Information, Rutgers University, New Brunswick, New Jersey, United States

The final, formatted version of the article will be published soon.

    Semantic network analysis is an important tool researchers can use to untangle the knots of tension that arise as communities debate and discuss complex issues. Yet words connect not only to each other in community discourse but to larger themes or issues. In this paper, we demonstrate the use of multilayer analysis for the study of semantic networks, helping to unravel connections within and between community tensions. In examining knotted tensions that arise in the wake of disaster, this study also spotlights how climate disasters exacerbate issues like housing equity, disproportionately affecting lower-income communities. We examine discourse across eight months of online neighborhood threads about community issues in the aftermath of Hurricane Ida. We identify core tensions related to environmental sustainability, overdevelopment, neighborhood identity preservation, and economic vitality. Our within-tension analysis reveals the community's struggle with such dilemmas, while our between-tension analysis shows the interconnectedness of these issues. Our approach highlights which stakeholders are best positioned to address specific community problems. The findings challenge the conventional top-down disaster response narrative, proposing a theoretically informed method for employing semantic network analysis to examine community crises. Through this work, we extend organizational communication theories of knotted tensions, offering a nuanced lens to community discourse in the face of wicked problems.

    Keywords: Multilayer networks, Semantic network analysis, Bipartite networks, Disaster communication, Paradoxical tensions, Social Media, Text analysis & mining, computational social science

    Received: 15 Apr 2024; Accepted: 29 Oct 2024.

    Copyright: © 2024 Randazzo, Shugars, Acosta and Doerfel. 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: Casey Randazzo, School of Communication and Information, Rutgers University, New Brunswick, New Jersey, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.