2023年度日本認知科学会第40回大会

P1-034COVID-19
Decline in accuracy discernment of COVID-19 related articles over time
1*

23

Kazunori Fujimoto, Yuko Tanaka, and Miwa Inuzuka
123

Kindai University, Nagoya Institute of Technology, Tokyo Gakugei University*Corresponding author: kfujimoto@kindai.ac.jp[5]
COVID-19[6]

Roozenbeek

COVID-19

COVID-19
Roozenbeek
923

2020

302023

2

Pennycook

[4]

26

1.

Fujimoto[1,2]
accuracy nudge

2.

[3]

2.1
[4]

2.1 Roozenbeek

Roozenbeek

Roozenbeek[5]3Study 12020

PennycookCOVID-19[4]

Systematizing Confidence in OpenCOVID-19

Study 2
Study 1
Study 1

Research and Evidence SCORE
Roozenbeek

[5]

30

[4]

1515570

attunedRespondi
169

COVID-1912023年度日本認知科学会第40回大会

P1-034「.1()Age, Gender, Reside,Ethnicity, Education,Income烝䕺𤩍磽慕𤩍楝撤渝𤩍褊踞𤩍讙瘓烝豬𤩍烝率砅溜痏ェ

COVID-19疰跚癉𤺋矞 燵砅0-100𤹪燈痮瘓⓮霙膅䅈箐磹穵窹籞𤹪莉暼䐢

covid

3.1

譏汲 乃瞤砅𦥯漣艹𤹪 燈痮瘓⓮霙舢
Strongly Democratic (-2.5), Democratic(-1.5), Lean Democratic (-0.5), Lean
Republican (0.5), Republican (1.5),
Strongly Republican (2.5)
不杞疰 癮 宙(real)㿉廃癮 宙(fake)㽷瞢𤺋矞

DemReptype

accuracy

宙疰 曚㿉磔畽㽷癉瘨畱瘳𤩍Yes(1)𤩍No膅-1䐢𤹪 燈痮瘓⓮霙膅Fujimoto瞢癮冨 曚滽𤹪皕5-point 箛穖祊籞穪楮燵𤹪 暼痮瘓瘓𥇍𤩍1,2砅-1𤩍3砅0𤩍4,5砅1㿉廸蜃痮瘳母 痮瘓䐢dateRoozenbeek瞢癮冨 曚滽癮迯鑢(R2020)㿉𤩍Fujimoto瞢癮冨 曚滽癮迯
鑢膅F2023䐢㽷瞢𤺋矞(1)-(3)accuracy =

type

(1)

accuracy =

date

(2)

accuracy =

type

date

accuracy date type(1)
0.716***(0.012)type(real)date(F2023)

Roozenbeek

Constant

COVID-19653
2022

(2)

(3)
0.760***(0.014)0.184***(0.020)-0.227***(1)[4](0.029)-0.432***(0.010)21220
0.138(real)(fake)

Health Feedback2

Pennycook1140--0.397***-0.052***(0.009)(0.008)21220
21220
0.1350.001* p < 0.05, ** p < 0.01, *** p < 0.001
Note: Standard errors are in parenthesis.

Num.Obs.
R2

12

PolitiFact1

2.1

0.070***(0.015)type(real)× date(F2023)

30(3)

3.1
3.1

2.2 Fujimoto
Fujimoto

type date[4]
0.716, p < 0.001

Prolific

2.1

-1

1

Roozenbeek(2)(F2023)

Fujimoto
Roozenbeek(R2020)0.070,
12p < 0.001

Roozenbeek(3)(type)(date)

30-0.227, p < 0.001+0.184

3.

-0.043=0.184-0.227
Roozenbeek
1530
2020

9152023

3.2

2

Roozenbeek

COVID-19

COVID-19+0.184

https://www.politifact.com/
2 https://healthfeedback.org/
1170

2023年度日本認知科学会第40回大会

P1-034

3-0.043

4.1(4)accuracy

discernment =discernment

(4)

date

discernment date

2.1

[4,5]
4.1
4.1
(4)
-0.227***(0.035)Constant
0.760***(0.022)
984
Num.Obs.
R20.041* p < 0.05, ** p < 0.01, *** p < 0.001
Note: Standard errors are in parenthesis.

Roozenbeek

date(F2023)

COVID-19

4.

(4)

2020

2023

2-0.227, p < 0.001

2020
4.2

99

0.76

29.9%

(1)

covid

(2)

DemRep4.2

3(1)(5) (6)

discernment
covid =
discernment =discernment :(5)

date
date

covid

covid date discernment

date covid
2.1

4.2
4.2
(5)
-24.345***(1.865)date(F2023)
covid
covid
× date(F2023)

Roozenbeek(0.001)71.942***0.738***(0.996)(0.062)
1174
Num.Obs.
983
R20.137
0.069* p < 0.05, ** p < 0.01, *** p < 0.001
Note: Standard errors are in parenthesis.

1-2 =-1-1

Fujimoto

5-point-1, 0, 1

2.10.004***

Constant-1
2 =1-(-1)(6)-0.416***(0.082)0.000(0.001)(5)2020

Roozenbeek

COVID-1992023-24.345, p < 0.001

1712

(6)

2023年度日本認知科学会第40回大会(6)

P1-034COVID-19
2020

92023

COVID-19

covid
2
covid

24.3450.097 =24.345ェ(0.004+0.000)
date-0.416, p < 0.001

23%

(2)
(7) (8)
DemRep =datediscernment =date(7)
DemRep(8)date DemRep

DemRep date discernment

4.1 - 4.3

2.1

4.3
4.3
(7)
-0.627***(0.087)date(F2023)(8)-0.366***(0.040)-0.014(0.015)

DemRep
DemRep× date(F2023)-0.159***[5](0.025)Constant-0.199***0.758***(0.057)(0.022)
Num.Obs.
1171
980
R20.0390.116* p < 0.05, ** p < 0.01, *** p < 0.001
Note: Standard errors are in parenthesis.

(7)[1]2023

22020
(6)
(8)
92023

DemRep2DemRep0.627

0.11 =0.627ェ(0.159+0.014)
datep < 0.001,,. (2022).

.
, 29(3), 509–527.
https://doi.org/10.11225/cs.2022.003[2] Fujimoto, K., Tanaka, Y., & Inuzuka, M. (2022). Selective
Intervention Strategy Based on Content Perception Model
Against Fake News Sharing. In 2022 Joint 12th International
Conference on Soft Computing and Intelligent Systems and23rd International Symposium on Advanced Intelligent
Systems(SCIS&ISIS)
(pp.
1–8).
https://doi.org/10.1109/SCISISIS55246.2022.10002015[3] Pennycook, G., & Rand, D. G. (2022). Nudging Social Media
toward Accuracy. The ANNALS of the American Academy ofPolitical and Social Science, 700(1), 152–164.
https://doi.org/10.1177/00027162221092342[4] Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D.
G. (2020). Fighting COVID-19 Misinformation on Social
Media: Experimental Evidence for a Scalable Accuracy-Nudge
Intervention. Psychological Science, 31(7), 770–780.
https://doi.org/10.1177/0956797620939054[5] Roozenbeek, J., Freeman, A. L. J., & van der Linden, S. (2021).
How Accurate Are Accuracy-Nudge Interventions? A
Preregistered Direct Replication of Pennycook et al. (2020).
PsychologicalScience,32(7),1169–1178.
https://doi.org/10.1177/09567976211024535[6] Fazio, L. K., Brashier, N. M., Payne, B. K., & Marsh, E. J.
(2015). Knowledge does not protect against illusory truth.
Journal of Experimental Psychology: General, 144(5), 993–1002. https://doi.org/10.1037/xge0000098

9-0.627, p < 0.001

2020

19K03253-0.366,30%

5.
COVID-19172