Citation impact and social media visibility of Great Barrington and John Snow signatories for COVID-19 strategy

IoannidisJP. BMJ Open 2022;12:e052891. doi:10.1136/bmjopen-2021-052891

Open access

Citation impact and social media

visibility of Great Barrington and John

Snow signatories for COVID- 19 strategy

John P Ioannidis

To cite: IoannidisJP. Citation

impact and social media

visibility of Great Barrington

and John Snow signatories for

COVID- 19 strategy. BMJ Open

2022;12:e052891. doi:10.1136/

bmjopen-2021-052891

► Prepublication history for

this paper is available online.

To view these les, please visit

the journal online (http://dx.doi.

org/10.1136/bmjopen-2021-

052891).

Received 28 April 2021

Accepted 25 January 2022

Departments of Medicine, of

Epidemiology and Population

Health, of Biomedical Data

Science, and of Statistics,

Stanford University, Stanford,

California, USA

Correspondence to

Dr John P Ioannidis;

jioannid@ stanford. edu

Original research

employer(s)) 2022. Re- use

permitted under CC BY- NC. No

commercial re- use. See rights

and permissions. Published by

BMJ.

ABSTRACT

Objective The Great Barrington Declaration (GBD) and the

John Snow Memorandum (JSM), each signed by numerous

scientists, have proposed hotly debated strategies for

handling the COVID- 19 pandemic. The current analysis

aimed to examine whether the prevailing narrative that

GBD is a minority view among experts is true.

Methods The citation impact and social media presence

of the key GBD and JSM signatories was assessed.

Citation data were obtained from Scopus using a

previously validated composite citation indicator that

incorporated also coauthorship and author order and

ranking was against all authors in the same Science-

Metrix scientic eld with at least ve full papers. Random

samples of scientists from the longer lists of signatories

were also assessed. The number of Twitter followers for all

key signatories was also tracked.

Results Among the 47 key GBD signatories, 20, 19

and 21, respectively, were top- cited authors for career

impact, recent single- year (2019) impact or either. For

comparison, among the 34 key JSM signatories, 11,

14 and 15, respectively, were top cited. Key signatories

represented 30 different scientic elds (9 represented

in both documents, 17 only in GBD and 4 only in JSM).

In a random sample of n=30 scientists among the

longer lists of signatories, ve in GBD and three in JSM

were top cited. By April 2021, only 19/47 key GBD

signatories had personal Twitter accounts versus 34/34

of key JSM signatories; 3 key GBD signatories versus

10 key JSM signatories had >50 000 Twitter followers

and extraordinary Kardashian K- indices (363–2569). By

November 2021, four key GBD signatories versus 13 key

JSM signatories had >50 000 Twitter followers.

Conclusions Both GBD and JSM include many stellar

scientists, but JSM has far more powerful social media

presence and this may have shaped the impression that it

is the dominant narrative.

INTRODUCTION

The optimal approach to the COVID- 19

pandemic has been an issue of major debate.

Scientists have expressed different perspec-

tives and many of them have also been

organised to sign documents that outline

overarching strategies. Two major schools

of thought are represented by the Great

Barrington Declaration (GBD)

and the

John Snow Memorandum (JSM)

2 3

that were

released with a short time difference in the

fall of 2020. Each of them had a core team of

original signatories and over time signatures

were collected for many thousands of addi-

tional scientists, physicians and (in the case

of GBD) also citizens.

A careful inspection

is necessary to understand the differences

(but also potential common points) of the

two strategies.

4 5

The communication of these

strategies to the wider public through media

and social media has often created confusion

and tension. The communication includes

what endorsing scientists state and how oppo-

nents describe the opposite strategy. Oversim-

plification, use of strawman arguments, and

allusions of conflicts, political endorsements

and ad hominem attacks can create an explo-

sive landscape.

4–9

Briefly, GBD is focused on targeted protec-

tion of high- risk individuals, while JSM

considers that such a strategy may not be

achievable. Much tension

5–9

surrounds also

the concept of herd immunity, where GBD

declares that herd immunity is unavoidable

eventually (much like gravity is unavoidable),

while JSM stresses that aiming for herd immu-

nity through natural infection is unethical.

JSM proponents often accuse GBD propo-

nents as urging the population to be infected,

while GBD signatories deny this accusation.

The two schools also tend to differ in terms of

their approach towards lockdowns, seen in a

far more negative light in GBD than in JSM.

It is often stated in social media and media,

by JSM proponents in particular, that JSM is

by far the dominant strategy and that very few

Strengths and limitations of this study

► Citation impact metrics and Twitter followers can be

measured with relatively high accuracy.

► The analysis focused primarily on the key signatories.

► Both citation indices and Twitter followers have

limitations in face validity and construct validity as

measures of impact.

on September 12, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-052891 on 9 February 2022. Downloaded from on September 12, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-052891 on 9 February 2022. Downloaded from on September 12, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-052891 on 9 February 2022. Downloaded from

IoannidisJP. BMJ Open 2022;12:e052891. doi:10.1136/bmjopen-2021-052891

Open access

scientists with strong credentials endorse GBD.

6–9

GBD

proponents are often characterised as fringe, arrogant

and wrong by their opponents.

6–9

However, are these views

justified based on objective evidence on scientific impact

or they reflect mostly perceptions created by social media

and their uptake also by media?

Here, an analysis is being performed to try to evaluate

the scientific impact and the social media visibility of the

key signatories who have led the two strategies. Scientific

impact is very difficult to evaluate in all its dimensions

and no single number exists that can measure scientific

excellence and scholarship. However, one can use cita-

tion metrics to objectively quantify the impact of a scien-

tist’s work in terms of how often it is used in the scientific

literature. Adjustments for coauthorship patterns, relative

contributions and scientific field need to be accounted

for.

Concurrently, an additional analysis evaluated the

social media visibility of signatories, as denoted by Twitter

followers.

METHODS

Documents and signatories

The two documents were retrieved online.

1–3

For the main

analysis, the 47 original key signatories of the GBD who

were listed on its original release online, and the 34 orig-

inal key signatories who authored the first release of the

JSM in a correspondence item published in the Lancet

were considered for in- depth citation analysis.

The two documents have been signed by many more

signatories. As of 2 April 2021, the GBD site

listed the

following signature counts: 764 172 concerned citizens,

13 796 medical and public health scientists and 41 895

medical practitioners. However, detailed data on names

and affiliations were provided only on 443 medical and

public health scientists. As of 25 November 2021, signa-

ture counts included 811 461 concerned citizens, 15 019

medical and public health scientists and 44 541 medical

practitioners. As of 2 April 2021, the JSM site

listed 3600

names of signatories (expanded to 4200 as of 25 November

2021). The sets of 443 and 3600 names included also the

original 47 and 34 key signatories, respectively. A random

set of 30 names was selected from the 443 GBD names

and from the 443 first- listed JSM names on 2 April 2021,

acknowledging that the earlier listed names may be more

likely to include highly cited, prominent scientists.

Citation data

Citation analyses used data on a validated composite

citation indicator that considers six citation indicators

(total citations, Hirsch H- index, coauthorship- adjusted

Hm- index, total citations to single- authored papers, total

citations to single or first- authored papers, total citations

to single, first or last- authored papers).

10–12

Existing

databases were used that contain all authors who are in

the top 2% of their scientific field based on career- long

impact until the end of 2019 and based on impact in a

recent single year (2019).

Given that field assignment

is not perfect, scientists who are in the top 100 000 in

the composite citation indicator across all scientists across

all science are also included, regardless of whether they

reach the top 2% in their specific field. Data were avail-

able including and excluding self- citations, as previously

described,

11 12

and the latter are presented in the results,

unless otherwise specified. The databases are compiled

based on Scopus information on all authors who have at

least five full papers (articles, reviews, conference papers)

in their career (~8 million authors). Science is divided in

174 scientific fields according to the Science- Metrix classi-

fication that capitalises on the subject matter and journal

venues where articles appear.

Twitter information

For the 43 and 34 original key signatories, their names

were searched on Google to identify personal Twitter

accounts. Only accounts listed under their name were

eligible, excluding group or institutional accounts from

groups/institutions where they belonged or which they

may have led. The number of followers of eligible Twitter

accounts as of 2 April 2021 was recorded and an updated

search was performed on 25 November 2021.

Kardashian index calculations

The Kardashian K- index

is providing an impression

on whether the Twitter footprint of a scientist is dispro-

portionately high compared with the footprint of his/

her citation impact. It is calculated as the ratio of Twitter

followers divided by 43.3C

0.32

, where C is the total cita-

tions received in one’s career. The original publication

defining the index used citations from Google Scholar.

However, given that many signatories did not have Google

Scholar pages and Google Scholar citations may be more

erratic, Scopus citations (including self- citations) as of 2

April 2021 were used instead. Scopus citation counts may

be slightly or modestly lower than Google Scholar cita-

tions, and this may lead to slightly higher K- index esti-

mates, but the difference is probably small.

Patient and public involvement

There was no patient or public involvement in the study.

No patients were evaluated in the study.

RESULTS

Top-cited scientists among the key GBD and JSM signatories

Among the 47 original key signatories of GBD, 20, 19

and 21, respectively, were among the top- cited authors

for their career impact, their recent single- year (2019)

impact or either. Among the 34 original key signatories

of JSM, 11, 14 and 15, respectively, were among the top-

cited authors for their career impact, their recent single

year (2019) or either. The percentage of top- cited scien-

tists is modestly higher for GBD than for JSM, but the

difference is not beyond chance (p>0.10 for all three

definitions).

on September 12, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-052891 on 9 February 2022. Downloaded from

IoannidisJP. BMJ Open 2022;12:e052891. doi:10.1136/bmjopen-2021-052891

Open access

Table 1 shows the 36 top- cited scientists from the key

signatories of the two documents along with their primary

and secondary scientific fields and their ranking among all

scientists in their primary scientific field. As shown, when

both the primary and secondary fields were considered,

both documents had top- cited signatories representing

nine fields (Developmental Biology, Endocrinology &

Metabolism, Epidemiology, General & Internal Medicine,

Table 1 Top- cited scientists among the key signatories of the Great Barrington Declaration (GBD) and John Snow

Memorandum (JSM)

Scientists Primary ﬁeld Secondary ﬁeld

Single- year

rank (2019)

Career- long

rank

Scientists in

same ﬁeld

GBD

Walker, Alexander M Pharmacology & Pharmacy Epidemiology 353 78 94 611

Dalgleish, Angus G Oncology & Carcinogenesis Immunology 2632 922 230 678

Brookes, Anthony J Genetics & Heredity Developmental Biology 207 211 32 641

Janvier, Annie Pediatrics Applied Ethics 314 1065 49 820

Kotchoubey, Boris Neurology & Neurosurgery Experimental Psychology 5446 5595 227 881

Meissner, H Cody Pediatrics Microbiology 221 263 49 820

Katz, D L Public Health Nutrition & Dietetics 349 374 48 533

Livermore, David M Microbiology General & Internal Medicine 17 6 134 369

Shahar, Eyal Cardiovascular System &

Hematology

Neurology & Neurosurgery 945 1203 152 312

Kampf, Günter Epidemiology Microbiology 102 183 9540

Colhoun, Helen M Endocrinology & Metabolism Cardiovascular System &

Hematology

311 420 69 094

Ludvigsson, Jonas F Gastroenterology & Hepatology General & Internal Medicine 59 326 76 367

Ratcliffe, Matthew Philosophy Experimental Psychology 141 * 7775

Levitt, Michael Biophysics Bioinformatics 19 6 18 401

Hulme, Mike Meteorology & Atmospheric

Sciences

Geography 52 45 54 940

Majumder, Partha P Genetics & Heredity Evolutionary Biology 544 412 32 641

McKeigue, Paul Endocrinology & Metabolism Genetics & Heredity 1170 326 69 094

Wood, Simon N Statistics & Probability Ecology 9 29 16 942

Bhattacharya, Jay Health Policy & Services General & Internal Medicine 281 * 16 521

Kulldorff, Martin Statistics & Probability Public Health 58 34 16 942

Friedman, Eitan Oncology & Carcinogenesis Genetics & Heredity * 1974 230 678

JSM

Bogaert, Debby Microbiology Respiratory System 1812 * 134 369

Dowd, Jennifer B Epidemiology Public Health 141 * 9540

Goldman, Lynn R Toxicology Epidemiology * 531 45 124

Greenhalgh, Trisha Health Policy & Services General & Internal Medicine 171 22 106 795

Hanage, William P Microbiology Developmental Biology 628 1236 134 369

Kellam, Paul Virology Microbiology 1075 553 58 416

Krammer, Florian Virology Immunology 21 708 58 416

Lipsitch, Marc Microbiology Epidemiology 189 172 134 369

McKee, Martin Public Health General & Internal Medicine 22 19 48 533

Rutter, Harry Public Health Endocrinology & Metabolism 811 * 48 533

Smith, Tara C Microbiology Epidemiology 1595 * 134 369

Sridhar, Devi General & Internal Medicine Developmental Biology 1321 1825 106 795

Swanton, Charles Oncology & Carcinogenesis Developmental Biology 47 445 230 678

Walensky, Rochelle P Virology Microbiology 436 670 58 416

Yamey, Gavin General & Internal Medicine Tropical Medicine 860 766 106 795

*Not in the top 2% of the eld or top 100 000 across all scientists and all science for the specic time frame.

on September 12, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-052891 on 9 February 2022. Downloaded from

IoannidisJP. BMJ Open 2022;12:e052891. doi:10.1136/bmjopen-2021-052891

Open access

Health Policy & Services, Immunology, Microbiology,

Oncology & Carcinogenesis, Public Health). Conversely

17 fields were represented only by key GBD signatories

(Applied Ethics, Bioinformatics, Biophysics, Cardio-

vascular System & Hematology, Ecology, Evolutionary

Biology, Experimental Psychology, Gastroenterology &

Hepatology, Genetics & Heredity, Geography, Meteo-

rology & Atmospheric Sciences, Neurology, Nutrition &

Dietetics, Pediatrics, Pharmacology & Pharmacy, Philos-

ophy, Statistics & Probability) and four fields were repre-

sented only by key JSM signatories (Respiratory System,

Tropical Medicine, Toxicology, Virology).

Random samples of scientists from the longer list of

signatories

In a random sample of n=30 scientists among the longer

list of GBD signatories, five were included in the data-

bases of top- cited authors (in career- long and/or recent

single- year citation impact), while this was true for n=3

of 30 JSM controls, a difference not beyond chance

(p>0.10). These sampled scientists included three of

the key signatories (Helen Colhoun and Michael Levitt

(GBD) and Martin McKee (JSM)), and five additional

ones (Dusko Ilic (Developmental Biology, Biochemistry

& Molecular Biology), Michael Jensen (Endocrinology &

Metabolism, General & Internal Medicine), Guy Hutton

(Tropical Medicine, Health Policy & Services) in GBD;

David Schwappach (General & Internal Medicine, Health

Policy & Services) and Jose M Martin- Moreno (General &

Internal Medicine, Nutrition & Dietetics) in JSM).

Excluding the key signatories, the proportions were

3/26 and 2/27 (p>0.10). The original key signatories

were far more likely to include top- cited scientists in the

GBD list (21/47 vs 3/26, p=0.004) and the same was true

also for the JSM list (15/34 vs 2/27, p=0.002).

Personal Twitter accounts

As of 2 April 2021, only 19/47 key GBD signatories had

a retrievable personal Twitter account, while every single

one of the 34 key signatories of JSM had a personal Twitter

account (p<0.001). The median number of followers of

the 34 JSM scientists was much larger than the median

number of followers of the 47 GBD scientists (31 600 vs 0,

p<0.001, figure 1).

Only 4/47 GBD signatories versus 17/34 JSM signato-

ries had over 30 000 Twitter followers (3/47 vs 10/34 for

signatories with over 50 000 Twitter followers). Twitter

and citation data, and inferred Kardashian K- indices for

the scientists with >50 000 followers appear in table 2. The

values of K- index in these scientists were extraordinarily

high (363–2569).

An updated search for Twitter accounts and followers

on 25 November 2021 found that 22/47 key GBD signa-

tories versus 34/34 key JSM signatories had a retriev-

able Twitter account (p<0.001). The median number of

followers was 0 vs 34 600 (p<0.001). The number of key

signatories with >50 000 followers was 13 vs 4.

DISCUSSION

An analysis of citation and social media impact of GBD

and JSM signatories shows that both documents have

been signed by many leading stellar scientists with very

high citation impact in the scientific literature. Random

sample data on the longer list of signatories suggest that,

expectedly, the longer lists are less thickly populated with

extremely highly cited scientists. The total number of

top- cited scientists cannot be compared for the two docu-

ments because the GBD site does not provide details on all

the signatories and signatures are still verified and vetted.

Thus, it is unclear whether the much larger total number

of signatures in GBD would also translate to a substan-

tially larger total number of top- cited scientists endorsing

it as compared with JSM. Regardless, GBD is clearly not

a fringe minority report compared with JSM, as many

social media and media allude.

6–9

GBD may be a more

commonly espoused narrative than the JSM narrative

among most cited scientists. Acknowledging uncertainty

given the fragmentary nature of the presented names of

signatories, it is safe to conclude that both documents

have been endorsed by many scientists who are very influ-

ential in the scientific literature.

Conversely, the two cohorts of key signatories have a

stark difference in Twitter follower counts. The majority

of key GBD signatories have no personal Twitter account

that could be readily identified. While it is possible

that some accounts might have been missed (eg, if not

directly named after the individual scientist’s names),

the difference is so major that it is very unlikely to be a

data retrieval artefact. Even among those GBD signato-

ries who do have Twitter accounts, very few have a high

number of followers. The key JSM signatories have a

Figure 1 Number of Twitter followers of John Snow

Memorandum (JSM) and Great Barrington Declaration (GBD)

key signatories in April 2021. Twenty- eight of the 47 GBD

signatories had no identied personal Twitter accounts.

on September 12, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-052891 on 9 February 2022. Downloaded from

IoannidisJP. BMJ Open 2022;12:e052891. doi:10.1136/bmjopen-2021-052891

Open access

very large number of followers in highly active personal

Twitter accounts. The most visible Twitter owners include

some of the most cited scientists in the analysed cohorts

(Trisha Greenhalgh, Marc Lipsitch, Florian Krammer,

Rochelle Walensky, Michael Levitt, Martin Kulldorff, Jay

Bhattacharya) and others who have little or no impact in

the scientific literature, but are highly remarkable and

laudable for their enthusiastic activism (eg, Dominic

Pimenta).

Previous work that introduced the Kardashian K- index

stated that K- index values above 5 suggest an overem-

phasis of social media versus scientific literature presence

and called such researchers ‘Science Kardashians’.

This

characterisation has not caught up with evolutions in the

last few years. Many signatories, especially of JSM, have

extraordinarily high K- index, with values in the hundreds

and thousands. However, one should account that the

volume of Twitter users and followers has increased mark-

edly since the K- index was first proposed, even before

the COVID- 19 pandemic and even for specialists in disci-

plines that are not very likely to attract massive social

media interest (eg, urology).

As COVID- 19 has attracted

tremendous social media attention, Kardashian K- indices

are skyrocketing. While no past data were available for

the number of followers of the analysed scientists pre-

COVID, anecdotal experience suggests that many, if not

most, saw their followers increase tremendously during

the pandemic. Substantial increases were documented

even in the short 7- month interval between April and

November 2021.

The massive advent of social media contributes to a

rampant infodemic

16–18

with massive misinformation

circulating. If knowledgeable scientists can have strong

social media presence, massively communicating accu-

rate information to followers, the effect may be highly

beneficial. Conversely, if scientists themselves are affected

by the same problems (misinformation, animosity, loss of

decorum and disinhibition, among others)

19 20

when they

communicate in social media, the consequences may be

negative.

The current analysis has several limitations. The anal-

ysis focused primarily on the key signatories and only a

small sample of the other signatories from the longer

lists was perused. More importantly, both citation indices

Table 2 Key signatories of John Snow Memorandum (JSM) or Great Barrington Declaration (GBD) with over 50 000 Twitter

followers as of April 2021: citation impact, H- index and K- index

Scientist

Twitter followers

(April 2021)*

Twitter followers

(November 2021)* Citations H- index K- index

Deepti Gurdasani (JSM) 50 400 103 900 5799 19 454

Angela Rasmussen (JSM) 210 800 283 900 1378 18 1931

Dominic Pimenta (JSM) 53 900 58 300 37 2 997

Trisha Greenhalgh (JSM) 121 700 150 000 28 003 81 689

Nisreen Alwan (JSM) 50 700 69 100 1059 19 456

Emma Hodcroft (JSM) 56 900 65 700 577 13 578

Florian Krammer (JSM) 192 600 232 300 11 288 61 1194

Marc Lipsitch (JSM) 226 900 235 800 23 565 82 1279

Devi Sridhar (JSM) 281 100 310 100 2720 23 2380

Rochelle Walensky (JSM) 90 000 345 800 10 561 54 580

Karol Sikora (GBD) 330 300 331 800 4401 30 2569

Michael Levitt (GBD) 86 900 105 300 40 731 106 451

Martin Kulldorff (GBD) 58 800 171 300 14 081 62 363

Only scientists with >50 000 Twitter followers as of April 2021 are shown in the table. As explained in the asterisk footnote below, by

November 2021, there were three more John Snow Memorandum key signatories (Isabella Eckerle, Zoe Hyde, Viola Priesemann) who

had increased their Twitter followers to >50 000 and one more Great Barrington Declaration signatory (Jay Bhattacharya) who had

acquired a Twitter account in the meanwhile and had also exceeded 50 000 Twitter followers. H- indices in November 2021 were 26 for

Isabella Eckerle, 22 for Zoe Hyde, 21 for Viola Priesemann and 37 for Jay Bhattacharya.

*Twitter followers for other key signatories in April 2021 and (in parenthesis) in November 2021: Rochelle A. Burgess 3281 (4504), Simon

Ashworth 8246 (9124), Rupert Beale 15 500 (19 200), Nahid Bhadelia 33 700 (39 400), Debby Bogaert 2574 (3030), Jenn Dowd 6933

(7221), Isabella Eckerle 48 800 (61 200), Lynn R Goldman 909 (922), Adam Hamdy 10 100 (12 000), William Hanage 39 500 (48 700), Zoe

Hyde 39 100 (55 300), Paul Kellam 1069 (1107), Michelle Kelly- Irving 10 200 (10 500), Alan McNally 16 300 (19 300), Martin McKee 33 800

(40 300), Ali Nouri 31 600 (34 600), Viola Priesemann 37 700 (54 900), Harry Rutter 8714 (8859), Joshua Silver 13 300 (15 800), Charles

Swanton 7724 (8721), Gavin Yamey 10 200 (26 100), Hisham Ziauddeen 2025 (9795) for JSM; and Andrius Kavaliunas 182 (3479), Ariel

Munitz 952 (1013), David Katz 46 000 (46 000), Eyal Shahar 2619 (6152), Gabriela Gomes 10 500 (14 400), Gerhard Krönke 69 (117),

Jonas Ludvigsson 2693 (7140), Lisa White 586 (642), Matthew Strauss 14 200 (22 700), Rajiv Bhatia 187 (1525), Salmaan Keshavjee 1955

(2213), Simon Thornley 520 (1207), Sylvia Fogel 614 (3405), Udi Qimron 2695 (4374), Yaz Gulnur Muradoglu 26 (39) for GBD. No personal

Twitter accounts were found for the remaining GBD signatories in April 2021, but three of them had detectable Twitter accounts in

searches done in November 2021 (Ellen Townsend 18 400 followers, Stephen Bremner 15 followers, Jay Bhattacharya 80 800 followers).

on September 12, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-052891 on 9 February 2022. Downloaded from

IoannidisJP. BMJ Open 2022;12:e052891. doi:10.1136/bmjopen-2021-052891

Open access

and Twitter followers have limitations in face validity and

construct validity as measures of impact. A lesser concern

is also that both can have errors of measurement, as

discussed below. The most important caveat is that scien-

tific impact is difficult to capture fully with any quantita-

tive metrics.

Specifically, citation indices do not capture necessarily

all aspects of scholarship.

The standardised, validated

composite index used here overcomes many of the limita-

tions of crude citation counting, but it is still not perfect.

For detailed description of the methods (and their valida-

tion) involved in selecting the top- cited scientists across

disciplines, one is referred to the background work done

to generate the lists of top- cited scientists.

10–12

Precision

and recall (author disambiguation in assigning papers)

are not perfect in Scopus, and some authors may have

underestimated or overestimated citation metrics, but

large errors are very uncommon.

Publications in Scopus

author profiles have 98.1% average precision (ratio of

publications correctly assigned to an author) and 94.4%

average recall (ratio of an author’s papers captured

compared with a gold set).

The precision for citation

linking in Scopus is measured at 99.9% and the recall is

98.3%.

Regardless, of the high technical accuracy of

these citation data, many scientists who are not included

in the lists of top- cited scientists may be at least as

outstanding as those who are included, and many dimen-

sions of scholarship, social responsibility and broader

impact may be missed by citation indices.

Twitter follower counts are practically impossible to

see as measures of excellence in the absence of context.

Social media impact may not necessarily be positive,

and massive misinformation and despicable behaviour

may still generate huge follower lists. Personal Twitter

accounts are easy to match against a specific person,

provided that the identity of that person can be discerned

in Twitter. One cannot exclude the possibility that some

of the people for which no Twitter account could be

identified may have a pseudonymous Twitter account

that hides their true identity. However, in this case, they

are not using their personal credentials and overall

expertise profile to support the credibility and validity

of their Twitter content. Moreover, some academics or

researchers may not have personal Twitter accounts, but

the centre, institute or other organisation they work in

may have some social media presence. The current anal-

ysis did not aim to capture these Twitter accounts, since,

by definition, they are not personal accounts, but serve a

very different role.

Acknowledging these caveats, the data suggest that the

massive superiority of JSM over GBD in terms of Twitter

firepower may have helped shape the narrative that it is the

dominant strategy pursued by a vast majority of knowledge-

able scientists. This narrative is clearly contradicted by the

citation data. The Twitter superiority may also cause, and/or

reinforce also superiority in news coverage. In a darker vein,

it may also be responsible for some bad publicity that GBD

has received, for example, as evidenced by plain Google

searches online or searches in Wikipedia pages for GBD, its

key signatories or even for other scientists who may espouse

some GBD features, for example, scepticism regarding the

risk- benefit of prolonged lockdowns. Smearing, even vandal-

isation, is prominent for many such Wikipedia pages or

other social media and media coverage of these scientists.

This creates a situation where scientific debate becomes vitri-

olic, and censoring (including self- censoring) may become

prominent. Perusal of the Twitter content of JSM signatories

and their op- eds suggests that some may have sadly contrib-

uted to GBD vilification.

A major point of attack has been alleged conflicts of

interest. However, GBD leaders have repeatedly denied

conflicts of interest (see also the site of GBD

). Key JSM

signatories appropriately and laudably disclosed upfront all

potential conflicts of interest in their original letter publica-

tion in the Lancet; the long list is available in public.

Based

on this list, it is possible that JSM leaders have more conflicts

than GBD leaders, but the social media superiority of JSM

controls also the narrative surrounding conflicts. A similar

vitriolic attack has been launched against the American

Institute of Economic Research that offered the venue for

hosting the launch of GBD.

Experimental studies show that

mentioning conflicts may have the same degree of negative

impact as attacks on the empirical basis of the science claims;

allegations of conflict of interest are as influential as allega-

tions of outright fraud, when the value of scientific evidence

is appraised.

Non- scientists’ trust is eroded by allusions of

conflicts of interest, while it is not affected much by percep-

tion of scientific (in)competence (which is also impossible

for a non- expert to appraise).

25 26

In good faith, reporting

of potential conflicts of interest should be encouraged and

transparency maximised. However, spurious allegations of

hidden agendas and conflicts should not become a weapon

for invalidating one or the other document. While excep-

tions may exist, probably the vast majority of scientists who

signed either document simply had good intentions towards

helping in a major crisis.

Number of signatures and/or scientific or other impact

and visibility of the signatories does not prove that a docu-

ment is correct. While such petitions are becoming increas-

ingly common in science,

it is erroneous to imagine

that scientific knowledge should be decided by crude,

expertise- weighted, citation- weighted or Twitter- weighted

vote counting.

Moreover, while both documents include

a massive number of stellar scientists, the vast majority of

the most influential scientists have not signed either docu-

ment. Some of them may be embarrassed to sign given the

adversarial, smearing environment that has emerged. Alter-

natively, many probably see that neither document contains

the perfect truth. And, of course, many scientists generally

abstain from signature collections.

Finally, while the data analysed here are limited to a

relatively small number of top- cited scientists, the eval-

uation of the key scientific fields where these scientists

publish offers some interesting hints. Both GBD and JSM

include top signatories in disciplines such as epidemi-

ology, public health and general and internal medicine

on September 12, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-052891 on 9 February 2022. Downloaded from

IoannidisJP. BMJ Open 2022;12:e052891. doi:10.1136/bmjopen-2021-052891

Open access

that are core pertinent fields in the pandemic. GBD has

more diversity in field expertise and includes top signa-

tories in quantitative disciplines such as statistics and

bioinformatics, as well as paediatrics and ethics that are

not represented among key JSM signatories, while JSM

has superior representation in virology. These patterns

may be due to chance given the relatively small sample

analysed, and given the many thousands of additional

signatories, these fields may well be represented in the

longer lists. However, these patterns could also reflect

some genuine differences in overall perspective between

the two strategies. For example, GBD focuses more on the

potential multifaceted collateral damage of lockdowns

and on prioritising quantitative assessment of risk (where

children and young people have far lower risk than

elderly, vulnerable people),

while JSM depends more

heavily on basic virology expertise. Given the magnitude

of the COVID- 19 crisis, it is important to ensure that

scientific disciplines can collaborate dispassionately and

that different views can be juxtaposed and integrated.

GBD and JSM may have more in common than it is often

thought. Critical differences between them should be

probed with rigorous science rather than defended on

partisan grounds and with social media warfare.

Contributors JPI conceptualised the original idea, collected the data, analysed the

data and wrote the manuscript. JPI is guarantor.

Funding The author has not declared a specic grant for this research from any

funding agency in the public, commercial or not- for- prot sectors.

Competing interests The author has signed neither of the two documents and

has many friends, collaborators and other people who he knows and he admires

among those who have signed each of them. JPI has previously published

that he is very skeptical about signature collection for scientic matters (BMJ

2020;371:m4048). He has no personal social media and he believes that the fact

that his citation indices are extremely high only proves (when compared against

his self- acknowledged vast ignorance) that these indices can occasionally be very

unreliable. JPI congratulates all the thousands of signatories (of both documents)

for their great sense of social responsibility.

Patient and public involvement Patients and/or the public were not involved in

the design, or conduct, or reporting, or dissemination plans of this research.

Patient consent for publication Not applicable.

Ethics approval This study does not involve human participants.

Provenance and peer review Not commissioned; externally peer reviewed.

Data availability statement All data relevant to the study are included in the

article. Data are available in a public, open access repository. All the data are in the

manuscript and tables and additional detail on citation data are available in publicly

deposited data sets in Mendeley.

Open access This is an open access article distributed in accordance with the

Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license, which

permits others to distribute, remix, adapt, build upon this work non- commercially,

and license their derivative works on different terms, provided the original work is

properly cited, appropriate credit is given, any changes made indicated, and the use

is non- commercial. See:http://creativecommons.org/licenses/by-nc/4.0/.

ORCID iD

John PIoannidis http://orcid.org/0000-0003-3118-6859

REFERENCES

1 Great Barrington Declaration. Available: https://gbdeclaration.org/

[Accessed 3 Apr 2021].

2 John snow memorandum. Available: https://www.johnsnowmemo.

com/ [Accessed 3 Apr 2021].

3 Alwan NA, Burgess RA, Ashworth S, etal. Scientic consensus

on the COVID- 19 pandemic: we need to act now. Lancet

2020;396:e71–2.

4 Lenzer J. Covid- 19: Experts debate merits of lockdowns versus

"focused protection". BMJ 2020;371:m4263.

5 Burki TK. Herd immunity for COVID- 19. Lancet Respir Med

2021;9:135–6.

6 Gump BB. The great Barrington declaration: when arrogance leads to

Recklessness. Available: https://www.usnews.com/news/healthiest-

communities/articles/2020-11-06/when-scientists-arrogance-leads-

to-recklessness-the-great-barrington-declaration [Accessed 2 Apr

2021].

7 Archer SL. 5 failings of the Great Barrington Declaration’s dangerous

plan for COVID- 19 natural herd immunity. Available: https://

theconversation.com/5-failings-of-the-great-barrington-declarations-

dangerous-plan-for-covid-19-natural-herd-immunity-148975

[Accessed 2 Apr 2021].

8 Mandavilli A. A viral theory cited by health ofcials draws re from

scientists. Available: https://www.nytimes.com/2020/10/19/health/

coronavirus-great-barrington.html [Accessed 2 Apr 2021].

9 Lenzer J, Brownlee S. The COVID- 19 science wars. scientic

American. Available: https://www.scienticamerican.com/article/the-

covid-science-wars1/ [Accessed 2 Apr 2021].

10 Ioannidis JPA, Klavans R, Boyack KW. Multiple citation indicators

and their composite across scientic disciplines. PLoS Biol

2016;14:e1002501.

11 Ioannidis JPA, Baas J, Klavans R, etal. A standardized citation

metrics author database annotated for scientic eld. PLoS Biol

2019;17:e3000384.

12 Ioannidis JPA, Boyack KW, Baas J. Updated science- wide

author databases of standardized citation indicators. PLoS Biol

2020;18:e3000918.

13 Archambault E, Beauchesne OH, Caruso J. Towards a multilingual,

comprehensive and open scientic Journal ontology. Proceedings

of the 13th International Conference of the International Society

for Scientometrics and Informetrics (ISSI), Durban, South Africa,

2011:66–77.

14 Hall N. The Kardashian index: a measure of discrepant social media

prole for scientists. Genome Biol 2014;15:424.

15 Chandrasekar T, Goldberg H, Klaassen Z, etal. Twitter

and academic urology in the United States and Canada: a

comprehensive assessment of the Twitterverse in 2019. BJU Int

2020;125:173–81.

16 Ahmed N, Shahbaz T, Shamim A, etal. The COVID- 19 Infodemic: a

quantitative analysis through Facebook. Cureus 2020;12:e11346.

17 Cinelli M, Quattrociocchi W, Galeazzi A, etal. The COVID- 19 social

media infodemic. Sci Rep 2020;10:16598.

18 Zarocostas J. How to ght an infodemic. Lancet 2020;395:676.

19 Voggeser BJ, Singh RK, Göritz AS. Self- control in online discussions:

disinhibited online behavior as a failure to recognize social cues.

Front Psychol 2017;8:2372.

20 Cheng J, Bernstein M, Danescu- Niculescu- Mizil C, etal.

Anyone can become a troll: causes of Trolling behavior in

online discussions. CSCW Conf Comput Support Coop Work

2017;2017:1217–30.

21 Hicks D, Wouters P, Waltman L, etal. Bibliometrics: the Leiden

manifesto for research metrics. Nature 2015;520:429–31.

22 Baas J, Schotten M, Plume A, etal. Scopus as a curated, high-

quality bibliometric data source for academic research in quantitative

science studies. Quant Sci Stud 2020;1:377–86.

23 Rice DB, Raffoul H, Ioannidis JPA, etal. Academic criteria for

promotion and tenure in biomedical sciences faculties: cross

sectional analysis of international sample of universities. BMJ

2020;369:m2081.

24 Greenhalgh T, McKee M, Kelly- Irving M. The pursuit of herd immunity

is a folly – so who’s funding this bad science? Guardian 2021.

25 Barnes RM, Johnston HM, MacKenzie N, etal. The effect of AD

hominem attacks on the evaluation of claims promoted by scientists.

PLoS One 2018;13:e0192025.

26 Mayer RC, Davis JH, Schoorman FD. An integrative model of

organizational trust. Acad Manag Rev 1995;20:709–34.

27 Hardwicke TE, Ioannidis JPA. Petitions in scientic argumentation:

dissecting the Request to retire statistical signicance. Eur J Clin

Invest 2019;49:e13162.

28 Ioannidis JP. Scientic petitions and open letters in the era of

covid- 19. BMJ 2020;371:m4048.

29 Smith GD, Spiegelhalter D. Shielding from covid- 19 should be

stratied by risk. BMJ 2020;369:m2063.

on September 12, 2024 by guest. Protected by copyright.http://bmjopen.bmj.com/BMJ Open: first published as 10.1136/bmjopen-2021-052891 on 9 February 2022. Downloaded from

BMJ Open 2022;12:e052891corr1. doi:10.1136/bmjopen-2021-052891corr1

Open access

Correction: Citation impact and social media visibility of

Great Barrington and John Snow signatories for

COVID- 19 strategy

Ioannidis JP. Citation impact and social media visibility of Great Barrington and John

Snow signatories for COVID- 19 strategy. BMJ Open 2022;12:e052891. doi: 10.1136/

bmjopen- 2021- 052891.

1. The methods section does not indicate the statistical tests being used. The statistical

tests are: (i) the Fisher’s exact test for 2x2 tables (ii) the Mann- Whitney U test for

two groups.

2. The Kardashian K- index was originally presented in satirical tone in an article,

but

has been used in numerous studies as a measure of an author’s scholarly output

compared to their social media presence.

3. The competing interests declaration of the author has been disputed, particular-

ly the author’s relationships to researchers closely linked to the Great Barrington

Declaration, most notably Jay Bhattacharya and Scott Atlas. Please see the rapid

responses to the article for the criticisms and the author’s response. The author has

now provided a more detailed statement relating to his professional collaborations:

As of February 2022, the 443 signatories from GBD included four scientists with whom I

have co- authored, and three with Stanford affiliation. The respective first 443 signatories of

JSM included five scientists with whom I have co- authored, and 15 with Stanford affiliation.

I have co- authored COVID- 19 scientific papers with both GBD and JSM signatories (more with

the latter). I have more close ongoing collaborators and friends in JSM than GBD. According to

Scopus I have 6590 co- authors and probably>200 have signed GBD or JSM. I have learnt from

both JSM and GBD colleagues and I thank them all for sharing their wisdom. Some readers rumi-

nated on potential relationships specifically with Jay Bhattacharya (JB) and Scott Atlas (SA), so

I provide more in- depth details: I have co- authored five papers with JB, talked with him and met

in person several times, and enjoyed dinner together once (in April 2022). Comparatively, with

several JSM signatories I have co- authored more papers (up to 19), talked and met more often and

shared more meals. An interview (https://www. youtube. com/ watch? v= x0u8jWMluSk) highlights

my agreements and disagreements with JB. I have not co- authored with SA, I have talked a few

times with him, but haven’t met in person yet. I have interacted with several thousand people more

than with SA. I am among several thousands of Stanford faculty and staff who did not sign an

open denouncement letter against SA; the approximately 100 who signed include some of my best

friends and collaborators. I wish people with opposing views could meet and discuss dispassion-

ately 1 day, and I offer to moderate such discussions. I thank everyone who made well- intentioned

contributions to the COVID- 19 crisis.

Open access This is an open access article distributed in accordance with the Creative Commons Attribution Non

Commercial (CC BY- NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non- commercially,

and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given,

any changes made indicated, and the use is non- commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

permissions. Published by BMJ.

BMJ Open 2022;12:e052891corr1. doi:10.1136/bmjopen-2021-052891corr1

REFERENCE

1 Hall N. The Kardashian index: a measure of discrepant social media prole for scientists. Genome Biol

2014;15:424.

Correction