Accuracy of health-related information regarding COVID-19 on Twitter during a global pandemic

被引:17
作者
Swetland, Sarah B. [1 ]
Rothrock, Ava N. [2 ]
Andris, Halle [3 ]
Davis, Bennett [4 ]
Nguyen, Linh [5 ,6 ]
Davis, Phil [6 ]
Rothrock, Steven G. [5 ]
机构
[1] Univ Florida, Gainesville, FL USA
[2] Duke Univ, Durham, NC USA
[3] Florida State Univ, Tallahassee, FL 32306 USA
[4] Magnolia Reg Hlth Ctr, Dept Emergency Med, Corinth, MS USA
[5] Florida State Univ, Coll Med, Tallahassee, FL 32306 USA
[6] Dr P Phillips Hosp, Dept Emergency Med, Orlando, FL USA
关键词
COVID-19; pandemic; social media;
D O I
10.1002/wmh3.468
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
This study was performed to analyze the accuracy of health-related information on Twitter during the coronavirus disease 2019 (COVID-19) pandemic. Authors queried Twitter on three dates for information regarding COVID-19 and five terms (cure, emergency or emergency room, prevent or prevention, treat or treatments, vitamins or supplements) assessing the first 25 results with health-related information. Tweets were authoritative if written by governments, hospitals, or physicians. Two physicians assessed each tweet for accuracy. Metrics were compared between accurate and inaccurate tweets using chi(2) analysis and Mann-Whitney U. A total of 25.4% of tweets were inaccurate. Accurate tweets were more likely written by Twitter authenticated authors (49.8% vs. 20.9%, 28.9% difference, 95% confidence interval [CI]: 17.7-38.2) with accurate tweet authors having more followers (19,491 vs. 7346; 3446 difference, 95% CI: 234-14,054) versus inaccurate tweet authors. Likes, retweets, tweet length, botometer scores, writing grade level, and rank order did not differ between accurate and inaccurate tweets. We found 1/4 of health-related COVID-19 tweets inaccurate indicating that the public should not rely on COVID-19 health information written on Twitter. Ideally, improved government regulatory authority, public/private industry oversight, independent fact-checking, and artificial intelligence algorithms are needed to ensure inaccurate information on Twitter is removed.
引用
收藏
页码:503 / 517
页数:15
相关论文
共 63 条
[11]  
Centers for Disease Control and Prevention, 2020, STOP SPREAD RUM
[12]  
Chen Emily, 2020, JMIR Public Health Surveill, V6, pe19273, DOI 10.2196/19273
[13]   Pandemics in the Age of Twitter: Content Analysis of Tweets during the 2009 H1N1 Outbreak [J].
Chew, Cynthia ;
Eysenbach, Gunther .
PLOS ONE, 2010, 5 (11)
[14]   The COVID-19 social media infodemic [J].
Cinelli, Matteo ;
Quattrociocchi, Walter ;
Galeazzi, Alessandro ;
Valensise, Carlo Michele ;
Brugnoli, Emanuele ;
Schmidt, Ana Lucia ;
Zola, Paola ;
Zollo, Fabiana ;
Scala, Antonio .
SCIENTIFIC REPORTS, 2020, 10 (01)
[15]  
Development Approval Process, 2019, DRUGS
[16]  
Food and Drug Administration, 2019, UN DRUGS
[17]  
Frenkel S., NEW YORK TIMES
[18]   Social Media's Initial Reaction to Information and Misinformation on Ebola, August 2014: Facts and Rumors [J].
Fung, Isaac Chun-Hai ;
Fu, King-Wa ;
Chan, Chung-Hong ;
Chan, Benedict Shing Bun ;
Cheung, Chi-Ngai ;
Abraham, Thomas ;
Tse, Zion Tsz Ho .
PUBLIC HEALTH REPORTS, 2016, 131 (03) :461-473
[19]   Chinese social media reaction to the MERS-CoV and avian influenza A(H7N9) outbreaks [J].
Fung, Isaac Chun-Hai ;
Fu, King-Wa ;
Ying, Yuchen ;
Schaible, Braydon ;
Hao, Yi ;
Chan, Chung-Hong ;
Tse, Zion Tsz-Ho .
INFECTIOUS DISEASES OF POVERTY, 2013, 2 :1-12
[20]  
Gadde Vijaya, 2020, Twitter company blog