Evidence map›Paper›PMID 35194309›Full record

ArticleTelematics and informatics2022

Estimating cost of fighting against fake news during catastrophic situations.

Hanseul Jo, Soyeong Park, Dongcheol Shin, Jungwoo Shin, Changjun Lee

Abstract readNews
In one paragraph

Article in Telematics and informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Hanseul JoDepartment of Big Data Analytics, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin, Gyeonggi 17104, South Korea.
Soyeong ParkDepartment of Industrial and Management Systems Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin, Gyeonggi 17104, South Korea.
Dongcheol ShinDepartment of Industrial and Management Systems Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin, Gyeonggi 17104, South Korea.
Jungwoo ShinDepartment of Industrial and Management Systems Engineering, Department of Big Data Analytics, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin, Gyeonggi 17104, South Korea.
Changjun LeeMedia and Social Informatics, Hanyang University, 55 Hanyang-daero, Sangnok-gu, Ansan, Gyeonggi 15588, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the battle with COVID-19 continues, an Infodemic problem has been raised. Even though the distribution of false news in national disaster situations has been reported for a long time, little attention has been given to the quantitative research of the fake news problem from the audience's perspective. This study, therefore, aims to estimate how much tax taxpayers would gladly pay for a virtual public-run fact-checking system. Using a one-and-one-half bounded dichotomous choice contingent valuation method, a survey was conducted on 525 respondents in Korea, and the spike model was applied to distinguish zero willingness-to-pay (WTP). The results show that a household's WTP for the public fact-checking system is 10,652 KRW (9 USD), on average, in the form of income tax for five years. Given the amount is a regular payment in perpetuity, the total WTP is estimated at 23 billion KRW ($196 M) every year. The result also shows that an individual's WTP increases as his or her psychological damage caused by fake news is high, as well as his or her high reliance on news in a disaster situation.

Indexed as

Contingent valuationFake newsFake news policyNational disaster situationNational hazardTechnological response in public

Identifiers

PMID35194309
PMCPMC8849195

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.