Evidence map›Paper›PMID 42073984›Full record

ArticleBehavioral sciences (Basel, Switzerland)2026

Disaster Emotion: When Media Messages Emphasize Self-Interested Responses.

Soyoung Kim, Christopher Stream, Suyeon Lee

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2026. 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

3 authors.

Soyoung KimSchool of Liberal Arts, Seoul National University of Science and Technology, 172 Gongneung-dong, Nowon-gu, Seoul 01811, Republic of Korea.
Christopher StreamGreenspun College of Urban Affairs, University of Nevada, Las Vegas, NV 89154, USA.
Suyeon LeeCollege of Energy and Biotechnology, Seoul National University of Science and Technology, 172 Gongneung-dong, Nowon-gu, Seoul 01811, Republic of Korea.

Funding

Ministry of Education of the Republic of Korea and the National Research Foundation of Korea NRF-2020S1A5A2A03046573
6 · The paper itself

Abstract

Media coverage of disasters frequently frames self-interested behavior in contrast to collective responsibility and coordinated response. This study aims to explore how such behavior is emotionally constructed in disaster-related media, using a carefully selected corpus of 12 text-centered news articles focusing on selfish behavior. The analysis combines transformer-based sentence-level emotion classification using the tweetnlp RoBERTa model, which predicts 11 emotion categories, with Latent Dirichlet Allocation topic modeling across single-sentence and three-sentence windows in a small purposively selected corpus. Emotion-topic relationships are quantified by weighting emotion probabilities by topic distributions and visualized using bar charts, network graphs, and heatmaps. The findings suggest that fear and disgust dominate portrayals of self-interested behavior, while anticipation appears in projections of harm and anger is linked to inequality and institutional accountability. Two discursive configurations emerge: Responsibility Across Individuals and Institutions, emphasizing public accountability and authority, and Collective Fear and Self-Protective Practices, reflecting affect-driven responses under uncertainty. Although negative emotions predominate, optimism appears conditionally, signaling coordination and recovery. Overall, disaster reporting constructs selfishness through integrated emotional-semantic patterns that position individual actions within broader social risk and collective responsibility.

Indexed as

disaster communicationmedia discourseself-interestselfishnesssentiment analysistopic modeling

Identifiers

PMID42073984
PMCPMC13114058

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.