Evidence map›Paper›PMID 39687572›Full record

ArticleFrontiers in research metrics and analytics2024

The multilayer semantic network structure of community tensions.

Casey Randazzo, Sarah Shugars, Rachel M Acosta, Marya Doerfel

Abstract read
In one paragraph

Article in Frontiers in research metrics and analytics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Casey RandazzoDepartment of Communication, School of Communication and Information, Rutgers University, New Brunswick, NJ, United States.
Sarah ShugarsDepartment of Communication, School of Communication and Information, Rutgers University, New Brunswick, NJ, United States.
Rachel M AcostaDepartment of Communication, School of Communication and Information, Rutgers University, New Brunswick, NJ, United States.
Marya DoerfelDepartment of Communication, School of Communication and Information, Rutgers University, New Brunswick, NJ, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: 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. Methods: 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. Results: 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.

Indexed as

bipartite networkscomputational social sciencedisaster communicationmultilayer networksparadoxical tensionssemantic network analysissocial mediatext analysis and mining

Identifiers

PMID39687572
PMCPMC11646995

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.