# Misrepresenting scientific consensus on COVID-19: The amplification of
dissenting scientists on Twitter

Alexandros Efstratiou
University College London
Tristan Caulfield
University College London

###### Abstract

The COVID-19 pandemic has resulted in a slew of misinformation, often
described as an “infodemic”. Whereas previous research has focused on the
propagation of unreliable sources as a main vehicle of misinformation, the
present study focuses on exploring the role of scientists whose views oppose
the scientific consensus. Using Nobelists in Physiology and Medicine as a
proxy for scientific consensus, we analyze two separate datasets: 15.8K tweets
by 13.1K unique users on COVID-19 vaccines specifically, and 208K tweets by
151K unique users on COVID-19 broadly which mention the Nobelist names. Our
analyses reveal that dissenting scientists are amplified by a factor of 426
relative to true scientific consensus in the context of COVID-19 vaccines, and
by a factor of 43 in the context of COVID-19 generally. Although more popular
accounts tend to mention consensus-abiding scientists more, our results
suggest that this false consensus is driven by higher engagement with dissent-
mentioning tweets. Furthermore, false consensus mostly occurs due to traffic
spikes following highly popularized statements of dissenting scientists. We
find that dissenting voices are mainly discussed in French, English-speaking,
Turkish, Brazilian, Argentine, Indian, and Japanese misinformation clusters.
This research suggests that social media platforms should prioritize the
exposure of consensus-abiding scientists as a vehicle of reversing false
consensus and addressing misinformation stemming from seemingly credible
sources.

_Keywords_ Misinformation $\cdot$ False consensus $\cdot$ Amplification
$\cdot$ Social networks

##  1 Introduction

As of November 2021, the COVID-19 virus has infected over 252M people and
claimed over 5.1M lives. The vaccination program against the pandemic is still
ongoing, with 7.16B vaccine doses having been administered so far [2].
However, hesitancy about these particular vaccines is alarming, with
percentages of at least somewhat concerned citizens as high as 46.3% in Italy,
43.1% in the US, and 41.1% in France [40]. While these vaccines are being
recommended by official health organizations based on scientific consensus
around their safety and efficacy, there seems to be some concern even among a
minority of medically literate people. Recently, Lucia and colleagues [31]
showed that COVID-19 vaccine hesitancy rates among a sample of medical
students was at about 23%.

Much like is the case with climate change [9], it is likely that the minority
of scientists who dissent from consensus may disproportionately be shaping
public opinion on COVID-19. Pickles et al. [35] have found that belief in
COVID-19 misinformation is associated with distrust towards official
government and scientific institutions. Similarly, people who trust scientists
and “conventional wisdom” about COVID-19 are less susceptible to
misinformation [17, 37]. If a scientist opposes consensus, they could be
perceived as less “corrupt” and more trustworthy by individuals who do not
subscribe to official institutions. Similarly, Salali and Uysal [39] have
reported that COVID-19 vaccine acceptance is associated with higher belief in
official statements about the pandemic, for example on the origins of the
virus. Dissenting scientists could therefore be lending credibility to
otherwise more conspiratorial or unsubstantiated scenarios. Generally, people
with lower educational levels seem to be more hesitant to COVID-19 vaccines,
perhaps because of misconceptions around the scientific method and scientific
consensus on the vaccines [32, 36].

This line of research complements other studies which suggest that social
media are the primary propagation platforms for COVID-related misinformation.
In a South Korean sample, Lee et al. [24] find that the majority of exposure
to COVID-19 misinformation occurs through social networking websites or
messaging platforms, especially for people with lower knowledge about COVID-19
who may be more impressionable. Consistent with this, another study reports
that higher engagement with social media increases susceptibility to COVID-19
misinformation [11]. People who consult unverified news sources have been
found to have higher COVID-19 vaccine hesitancy rates and to be reactant to
calls for vaccination in the UK [34]. In other research [6], it is shown that
performing online searches in a reactive manner to false COVID-19 information,
for example looking up things such as “chloroquine” or “man-made virus”,
correlates with higher coronavirus deaths per capita in six countries (Spain,
UK, US, Italy, Australia, and Germany).

Indeed, social media platforms do seem to be very misinformation-laden
environments. Qualitative analyses of small Tweet samples have placed the
prevalence of COVID-19 misinformation on Twitter at about 25% [23], while
larger-scale, automated analyses have identified approximately 70% of the
early information on COVID-19 to be non-credible [3]. Others have shown that
COVID-19 misinformation is more prevalent than fact-checks about this
misinformation in 35 countries [12], while unverified information on Twitter
in general proliferates faster than fact-checked information [46].

Evidently, we are facing a two-pronged problem. People with lower trust in
official institutions, as well as those who are less knowledgeable and
educated on COVID-19, are more prone to misinformation. At the same time, this
misinformation seems to mostly proliferate on social media, where content
creation is not bound by rigorous journalistic standards. In the present
study, we investigate whether the voices of dissenting scientists are
disproportionately amplified on social media. If this is truly the case, then
ostensible experts in a field can lend credibility to ideas that are rejected
or at least not supported by the wider scientific community. While prior
research has focused on untrustworthy news sites or conspiratorial tendencies
as the main vehicles of misinformation, here we are examining another
phenomenon: False consensus. We define false consensus as the deviation of
public opinion from true scientific consensus; in other words, the
disproportionate amplification of scientists who do not agree with scientific
consensus on Twitter. In turn, this can inform science communication efforts
around the proportionality of scientist exposure depending on their scientific
views.

Specifically, we attempt to answer the following research questions:

  1. RQ1:

On Twitter, are dissenting scientists amplified more relative to consensus-
abiding scientists in the context of COVID-19 vaccines and COVID-19 more
generally? What is the extent of this false consensus, if any?

  2. RQ2:

What drives exposure to different scientific views on Twitter? Is exposure
proportional to the popularity of the accounts that spread these views?

  3. RQ3:

What are the characteristics of the retweet network that spreads these views
across the Twitter platform?

##  2 Literature review

###  2.1 The role of scientific information

Public understanding of scientific consensus is an important endeavor and a
potentially effective vehicle in addressing misinformation. For example, the
sheer amount of often conflicting information about COVID-19 by either actual
or ostensible experts can drive a lot of confusion among the public [29].
Furthermore, Loomba et al. [30] find that, while COVID-19 misinformation in
general has the capacity of driving vaccine hesitancy, it is misinformation
attributed to scientists which is most effective in doing so (e.g.,
“Scientists express concerns over vaccines”). The opposite also holds.
Correcting perceptions of scientific consensus on climate change has been
found to increase belief in anthropogenic global warming [28], while the
acceptance of various scientific propositions (e.g. climate change, risk
factors of different health issues) have been found to be associated with
perceived scientific consensus on the respective topics [27].

This body of literature reveals two key implications. Firstly, a lot of
COVID-19 misinformation may simply be due to a misconception around the number
of scientists in support or in opposition of different approaches to battling
the pandemic; for example the number of scientists favoring lockdowns, and
those favoring a natural herd immunity approach. This is likely to be due to
both inherent confirmation biases, but also asymmetric information exposure.
Secondly, correcting these misconceptions around true scientific consensus may
be an effective way of eliciting more desirable behaviors from the public,
such as compliance with protective measures and vaccination, in order to
protect public health.

###  2.2 Cognitive biases and false consensus

The notion of scientific consensus, and by extension false consensus, remains
heavily understudied in the context of COVID-19. The false consensus effect
was originally reported by Ross et al. [38], who found that the average person
overestimates how many other people share their own convictions. Since then,
this has been demonstrated in various settings. Examples include climate
change deniers grossly overestimating the true percentage of other deniers in
the population [26], and US voters overestimating the percentage of Americans
who share their opinion on whether the 2020 US presidential election was
rigged [47].

With the advent of social media and online communications more generally,
false consensus may now be more prevalent. This is because some users on
social media tend to become enclosed in “echo chambers” where the same
opinions are reinforced [45]. In these cases, false consensus may become more
pronounced as individuals think their echo chambers are representative of the
entire information ecosystem [48, 50].

There is also evidence to suggest that false consensus can be caused by users’
sharing tendencies. Gallagher et al. [18] find that when it comes to COVID-19,
it is mostly public figures that receive the biggest exposure relative to
their Twitter activity. Medical professionals, on the other hand, are not
particularly prominent in terms of exposure. Crucially, Twitter users tend to
amplify figures which are either ideologically or demographically similar to
them, perhaps driving a false conception of how prominent certain opinions
are. It has also been found that those who are most prone to false consensus
also tend to believe that mainstream media are hostile against their opinions
[41]. In this case it is possible that, in an attempt to balance out this
ostensibly biased reporting of mainstream news, these people
disproportionately amplify dissenting voices to bring the information
ecosystem closer to their subjectively perceived consensus.

Perceived consensus, and any disconnect between it and true consensus, can
have significant effects on the pandemic response. We have already explained
how false consensus can lend credence to “scientific” opinion which is
otherwise unsupported by science, however it can also operate on the social
level. If a misguided group of people mistakenly believes that their opinion
is scientifically backed, then it should be easier to establish health norms
among this group. These perceived group norms, especially in more homogeneous
groups, can drive health and COVID-related behaviors in a consistent direction
[8, 19].

However, we do note research which has shown that it is individual beliefs,
not perceived community-wide norms that drive compliance intentions with
COVID-19 measures [25]. Therefore, it is important that these norms are
perceived about common-identity groups rather than broader, generic groups.

###  2.3 Previous work on COVID-19 misinformation

COVID-19 and the ensuing infodemic have attracted academic attention, however
most studies focus on either misinformation of a conspiratorial nature, or
automatic detection of misinformation.

Shahi et al. [42] analyze and classify misinformation based on fact-checked
claims, then reverse-search tweets which contain URLs with these fact-checked
claims. They perform exploratory analyses such as the emotional profile or the
most-used emojis in COVID-19 fact-checked misinformation tweets. Al-Rakhami
and Al-Amri [3] build a highly accurate classifier through feature extraction
using a dataset of early COVID-19 information, between January and April 2020.
Their classifier, based on user-level and tweet-level features, classifies
70.22% of early information as non-credible.

Singh et al. [44] map out the COVID-19 information landscape using a URL-based
approach where low-quality or high-quality information is labeled based on the
URL domain (i.e. high-quality, low-quality, or mainstream news providers).
They find that misinformation does exist on Twitter, however mainstream news
sources constitute a much bigger proportion. Mainstream news are also
connected to these low-quality and high-quality (i.e. health news) clusters,
therefore suggesting that they can be an avenue of exposure to either better-
or worse-quality information.

On conspiratorial information, Shahsavari et al. [43] analyze the emergence of
new COVID-19 conspiracy theories and plot a graph of their main communities on
4chan and Reddit. Similarly, Havey [21] analyzes a small sample of Tweets and
examines how political ideology can impact belief in COVID-19 conspiracy
theories. Memon and Carey [33] follow a keyword-based approach comprising
predominantly misinformation-laden terms and find that a significant
proportion of Twitter posts comes from bots, especially posts labeled as
misinformation. Yang et al. [49] also find that bots more commonly spread
misinformation than trustworthy sites, although the vast majority of
misinformation still comes from organic users.

##  3 Present study

Common to all of the above approaches is the origin of COVID-related
misinformation from unverified or conspiratorial sources. While misinformation
is, by default, false and unreliable, the concept of what constitutes a
verified or trustworthy source may differ between communities. For example, a
scientist would generally be associated with accuracy and trustworthiness,
however their claims could still be false if they are not backed up by
commensurate evidence. Therefore, while many of these approaches are based on
source as an indicator of trustworthiness (e.g. URL domain, account posting
the information, information appearing on fact-checking websites etc.), there
may be paradoxical situations where an otherwise credible individual spreads
false information.

In this study, we probe this potential phenomenon and examine if public
opinion deviates from scientific consensus on COVID-19 for the following
reasons. Firstly, is a unique opportunity to observe whether certain figures
can achieve popularity simply by virtue of being dissenting voices. Secondly,
it can highlight whether “naive science”, where the scientific credentials of
the source can trump the need for substantiating their claims, is weaponized.
Thirdly, and perhaps most importantly, it can highlight a need to move away
from considering misinformation purely in conspiratorial terms. Therefore,
this could point towards the need to more accurately reflect scientific
consensus on social media to prevent the asymmetric amplification of
dissenting voices.

###  3.1 Deriving a proxy for scientific consensus

There are two main challenges with deriving a scientific consensus on COVID-19
issues. First, due to the sheer number of people working on the pandemic, true
scientific consensus is difficult to pinpoint without prior research on the
topic (in contrast, lots of data are available on things like climate change
consensus; see [4, 13]). Second, scientists working on COVID-19 span multiple
disciplines (e.g. public policy, epidemiology, virology, social psychology,
etc.), with some being considered more prestigious or accomplished than
others. These are factors that an individual could consider when deciding
whose opinion they should consult. We note that Alwan et al. [5] did make a
call to establish consensus against natural herd immunity approaches, however
their list of signatories was quite small (80 people) relative to the entire
community of COVID-19, with signatories spanning multiple fields.

To address both problems of relative size and comparability of opinion, we opt
for a proxy approach. We restrict the eligible scientists to form part of our
ratio as those who have won a Nobel prize in Physiology or Medicine from 2000
onward. This leaves us with a manageable number of scientists whose opinions
we can qualitatively assess. Moreover, we reason that due to the prestige
associated with the Nobel prize, these scientists will be invoked mainly for
their laureate status. Therefore, while there is still quite a bit of variety
in the specific disciplines that Physiology and Medicine Nobel laureates work
in, we argue that the gravity of their opinions should be comparable among the
general public.

###  3.2 Conducting the search

First, we listed the names of Nobel laureates in Physiology or Medicine who
won the award from 2000 until 2020, as they appear on the official Nobel prize
website111https://www.nobelprize.org/prizes/lists/all-nobel-laureates-in-
physiology-or-medicine/. Out of 53 eligible laureates, we found that 44 were
still alive (i.e., no death date listed on their Nobel prize profiles). For
these laureates, we performed a Google search to find instances where they had
gone on record expressing an opinion on COVID-19 vaccines, looking at the
first three pages of results. We examined COVID-19 vaccines specifically to
maximize the comparability of opinions between the laureates and to have a
more exact frame of reference.

To examine the opinion of each eligible laureate on COVID-19 vaccines we used
the following search approach. Note that, while we removed the “vaccine”
keyword if no results of interest were returned to expand the search, we
continued searching the results for COVID-19 vaccine opinions specifically.
Keywords in OR statements were used sequentially; that is, if we received no
relevant results with the combination of the keywords “COVID-19” and
“vaccine”, we performed another search with the keywords “coronavirus” and
“vaccine”, then “COVID-19” and “vaccines”, and so on.

        1. ‘‘(Laureate name)’’
            AND
            (COVID-19 OR coronavirus)
            AND
            (vaccine OR vaccines)
                IF relevant results:
                    categorize laureate
                ELIF no relevant results:
        2. ‘‘(Laureate name)’’
            AND
            (COVID-19 OR coronavirus)

We examined all relevant results (i.e., those including at least 2 out of the
3 search terms in the title) regardless of medium or format (i.e., including
videos, blog posts, social media posts, etc). For laureates whose opinion we
could not find on Google, we also searched the platform DuckDuckGo. We did
this because of Google’s PageRank algorithm which prioritizes indexing based
on the trustworthiness and popularity of a news page. We reasoned that any
consensus-dissenting scientists may be given more exposure on unreliable news
sites, and therefore their statements could be pushed further down. To protect
from potential bias against consensus-dissenting scientists as a result of the
search platform we used (i.e. to avoid under-counting dissenting scientists),
we repeated the search query procedure on this platform which employs a
different search engine optimization.

###  3.3 Scientific consensus ratio

Out of the 44 Nobel laureates that we searched, we were able to find 25 who
had gone on record expressing an opinion on COVID-19 vaccines. Out of those,
24 were pro-vaccines, and just one was against. The one laureate who had
spoken against vaccines had gone on record multiple times, stating, among
other things, that vaccines cannot be considered safe if long-term side-
effects are unknown, and that the vaccines may cause antibody-dependent
enhancement (a phenomenon whereby vaccines can inadvertently increase the risk
and severity of infection).

Examples of pro-vaccine statements included support for the vaccination
program as the only way of ending the pandemic, calls to put faith in the
scientists and institutions who developed the vaccines, reassurances that the
vaccines are safe because their mechanisms are known and understood, signing
on open letters to expand access to vaccines to more impoverished countries,
and public appearances of laureates taking the vaccines themselves. The
sources consulted in this annotation procedure, as well as the names of the
laureates we were able to identify, are found in Appendix A.

Using this approach, we were able to derive a proxy for a ratio of scientific
consensus as $1:24$ for anti-vaccine to pro-vaccine scientists. We then
annotated tweets as anti if they mentioned the one laureate who appeared to be
against vaccines, and as pro if they mentioned any of the other 24. To ensure
that these tweets are not negative towards the laureates they discuss, we
performed a ground-truth validation of our approach in section 5. Based on
these, we calculated public consensus $c_{p}$ as
$c_{p}=\frac{anti_{total}}{anti_{total}+pro_{total}}$.

To calculate the factor of false consensus, $c_{f}$, we used the following
formula,

$c_{f}={\frac{c_{p}}{c_{t}}}$ (1)

where $c_{p}$ is the public (Twitter-derived) ratio and $c_{t}=\frac{1}{25}$.
The closer $c_{f}$ is to 1, the more public consensus resembles scientific
consensus.

##  4 Data

We utilized a keyword-based query in Twitter’s full-archive API to collect
three separate datasets.

###  4.1 Vaccine dataset

The first dataset regarded COVID-19 vaccine Tweets which mentioned at least
one of the 25 laureates that we identified in §3. Our search query consisted
of three key segments: Coronavirus synonyms, vaccine synonyms, and the
laureate names. The full query we used is seen in Appendix B.

We collected data between January 1st 2020 and July 5th 2021. In total, we
collected 15.8K tweets from 13.1K unique users mentioning at least one of the
laureates in the context of COVID-19 vaccines. 12.7K of these tweets were
retweets.

###  4.2 Generic COVID-19 dataset

Given the small size of the vaccine-specific dataset, we performed a second
query after removing the vaccine synonyms to broaden our search. While our
laureate annotations were about COVID-19 vaccines specifically, we conducted
another cursory search on the laureates’ general opinions. We were able to
find that the anti-vaccine laureate had also popularized unverified
information around the virus’ origins earlier in the pandemic, while none of
the other laureates had gone on record opposing consensus on COVID-19
generally before222The only exception was one of the pro-vaccine laureates who
had a quote falsely claiming the virus was created in a laboratory
misattributed to them. The laureate released a statement where they denied
having said this.. Therefore, we reasoned that the broader COVID-19 was a
comparable topic to COVID-19 vaccines specifically in this instance, and that
the potentially bigger sample would provide more context on the Twitter
discussion surrounding scientific discourse.

To maximize comparability, we also performed this search from January 1st 2020
until July 5th 2021. We were able to collect 208K tweets which mentioned at
least one of the 25 laureates in the context of COVID-19 from 151K unique
users. Out of these, 172.8K were retweets.

###  4.3 Baseline dataset

For comparison purposes, we also collected baseline data prior to COVID-19
becoming a prominent discussion topic on social media. For this purpose, we
removed both COVID-19 and vaccine-related terms from our search, keeping only
the laureate names. The baseline dataset included data from May 1st 2018 to
December 31st 2019. This was a comparable length of time to the other two
datasets. The end date was before the first recorded case of COVID-19 outside
of China when coronavirus became a worldwide topic of discussion, and right
before we began data collection for the other two datasets. In total, we
collected 196K baseline tweets by 136.9K unique authors, 159K of which were
retweets. We summarize all three datasets in Table 1.

Dataset | Tweets | Unique authors | % retweets | Data collection period
---|---|---|---|---
Vaccines | 15.8K | 13.1K | 80% | 01/01/2020 - 05/07/2020
COVID-19 general | 208K | 151K | 83% | 01/01/2020 - 05/07/2020
Baseline | 196K | 136.9K | 81% | 01/05/2018 - 31/12/2020
Table 1: Summary of datasets

##### Ethical considerations.

Use of Twitter API’s full archive search for this project was approved by the
official Twitter platform through a Twitter developer application. This
project received ethical approval from the UCL ethics committee, REC no.
Z6364106/2021/07/45. Data processing is performed in accordance with GDPR
provisions [1].

##  5 False consensus analysis

In this section, we attempt to answer RQ1 (to what extent are dissenting
scientists disproportionately amplified?) and RQ2 (is exposure to dissenting
scientists proportional to the popularity of the accounts which share their
opinions?).

###  5.1 Tweet annotation

First, we annotated the tweets in all datasets using the Nobel laureate names
that appeared in them. We followed a simple heuristic rule to do this. If a
pro-vaccine (pro-consensus) laureate name appeared in the tweet, then it would
be labeled as “pro”. If the anti-vaccine (anti-consensus) laureate’s name
appeared, then the tweet would be labeled as “anti”. Because of how the
Twitter API handles responses some of the tweets returned did not include the
laureate names in their text, but rather in associated meta-data (e.g. URL
link titles, embedded video titles, etc). Therefore, if our program could not
detect a laureate name in the main text of the tweet, it would also check if
the tweet included a URL, and if so, check the URL string for the Nobelist
names. The specific procedure was as follows:

    Check tweet text
    Count anti-laureate instances
    IF count > 0:
        return ‘‘anti’’
        STOP
    Count pro-laureate instances
    IF count > 0:
            return ‘‘pro’’
            STOP
    ELSE:
        Check if URL exists
        IF URL does not exist:
            return ‘‘undefined’’
        ELIF URL exists:
            Check URL string
            Count anti-laureate instances
            IF count > 0:
                return ‘‘anti’’
                STOP
            Count pro-laureate instances
                IF count > 0:
                    return  ‘‘pro’’
                    STOP
                ELSE:
                    return ‘‘undefined’’

Throughout this process, we iteratively checked any tweets which were
classified as “undefined” for alternative spellings of laureate names and
updated the annotation strings accordingly until we reached saturation.

##### Ground-truth testing.

Before proceeding with any further analyses, we constructed a validation set
for ground-truth testing from the generic COVID-19 dataset, which was broader
and therefore more representative. First, we filtered the data to only include
original (i.e. no retweets, quotes, or replies), English-language tweets which
had not been labeled as “undefined” during the automated annotation process.
From this subset, we randomly sampled 1000 tweets for manual annotation.
Annotation was performed by one researcher familiar with the laureates’
general views333We recognize that this may introduce some bias. The trade-off
is between potentially biased annotations and expertise with respect to
recognizing which scientists received exposure through these tweets, which we
attempted to incorporate in our annotation procedure.. The annotation
procedure followed a two-step priority method. First, we checked if the tweets
promoted consensus-dissenting information (e.g., human-made virus theories,
anti-vaccine sentiments, etc). If consensus-dissenting information was
advocated in the tweet, we labelled it as “anti”. If it remained unclear
whether the tweet was pro- or anti-scientific consensus, we treated it as
neutral in that respect. In that case, we would then check if the tweet
mentioned one of the 25 laureates in our list. If it did, we would annotate it
as “pro” or “anti” accordingly. Our reasoning here was that the tweet would be
giving exposure to the person it mentioned, and thus either supporting or
rejecting scientific consensus, respectively.

Using this annotation process, we were able to achieve an F1 score of 0.88
between our automated and ground-truth procedures. As this was deemed
satisfactory, we did not proceed with a calibration for our automated
annotation. The confusion matrix is shown in Table 2. Generally, we found that
the heuristic rule performed well in classifying consensus-dissenting and
consensus-abiding tweets. Examples of instances where the heuristic rule
failed were posts fact-checking claims by the anti-consensus laureate (thus
supporting scientific consensus despite the anti-laureate’s name appearing in
the tweet), and posts which mistakenly attributed misleading information to
pro-consensus laureates (thus going against scientific consensus despite the
pro-laureate’s name appearing in the tweet).

| $pro_{manual}$ | $anti_{manual}$
---|---|---
$pro_{auto}$ | 450 | 56
$anti_{auto}$ | 61 | 421
Table 2: Confusion matrix for ground truth annotation. We manually labeled 12
tweets as “undefined” and therefore removed them from the F1 analysis.

###  5.2 True vs. public consensus deviation

Next, we performed a simple frequency analysis of pro and anti tweets in all
three of our datasets. To calculate a factor of false consensus, we used
Equation 1, where $c_{p}$ was derived from the frequencies (absolute numbers)
of pro and anti tweets accordingly. The actual number of tweets, along with
the expected number of tweets based on $c_{t}$ and the deviation factor
between actual and expected numbers (i.e. false consensus, $c_{f}$) is seen in
Table 3.

Dataset | Expected anti | Actual anti | Expected pro | Actual pro |  $c_{f}$ factor
---|---|---|---|---|---
Vaccines | 590.32 | 13971 | 14167.68 | 787 | 426.05
General | 7499.12 | 120193 | 179978.88 | 67285 | 42.9
Baseline | 6626.08 | 6192 | 159025.92 | 159460 | 0.93
Table 3: False consensus factor, $c_{f}$, calculated from expected frequencies
based on $c_{t}$ and actual frequencies $c_{p}$. Sum of actual pro and actual
anti tweets is lower than the number of total tweets in Table 1 due to
instances of “undefined” tweets.

As seen in the table, the baseline dataset was generally in line with the
scientific consensus ratio $c_{t}$, with the expected and actual values being
quite close ($c_{f}=0.93$). Since the $c_{f}$ value for baseline was very
close to 1, we can say that public discourse on Twitter prior to the COVID-19
pandemic was “consensus-abiding”. In other words, any deviations from 1 in the
$c_{f}$ of the other two datasets is unlikely to be due to prior differences
in the popularity of the laureates.

With respect to the vaccine dataset, we can see a very large
disproportionality such that expected and observed values are almost reversed;
that is, the actual anti values approximate the expected pro values, and the
expected anti values are close to the actual pro values. This results in a
very large $c_{f}$, such that the anti-consensus laureate is amplified by a
factor of 426.05. A similar—albeit smaller-scale—pattern is observed with
regards to the general COVID-19 dataset. In this broader context, the anti-
consensus laureate is amplified by 42.9 times more relative to the consensus-
abiding laureates. Our analysis demonstrates that public consensus deviates
from scientific consensus to a very large extent in both the context of
COVID-19 vaccines and COVID-19 generally, especially for the former.

###  5.3 Time series analysis

To get a better idea of the dynamics regarding when these scientists are
discussed on Twitter, we created time-series plots at a weekly granularity. We
first plot absolute (Figure 1a) and log-transformed (Figure 1b) frequency
time-series comparisons between the baseline and COVID-19 general datasets. We
omit the vaccine dataset from this comparison due to its much smaller, and
thus non-comparable size.

(a) Absolute comparison.

(b) Log comparison.

Figure 1: Frequency comparisons between baseline and general COVID-19
datasets.

Looking at Figure 1a, we can see that for all years included in the datasets
there are spikes in pro tweets around the month of October (2018, 2019, 2020).
From a cursory examination of our data, we find that these spikes are due to
the official Nobel prize announcements for the respective year which tend to
attract a lot of attention and are widely retweeted. We also observe a mild
spike in pro tweets around May 2020. This concerned a story circulating on
social media which falsely claimed that one of the pro-consensus laureates
said that the virus was manufactured. On April 27th 2020, this laureate
released a statement denying having said
this444https://www.kyoto-u.ac.jp/en/news/2020-04-27. The spike which followed
was mainly due to the original story being picked up and debunked by fact-
checkers, which was subsequently shared widely on Twitter.

Regarding anti tweets, we again observe that they follow a spike-like pattern.
In this case, both of these spikes followed interviews with the anti-consensus
laureate which received a lot of attention. The first spike, around mid-April
2020, concerns a public appearance of the anti-consensus laureate on CNEWS, a
popular French news channel which has been said to promote conservative and
far-right views. In this appearance, dated April 17th 2020, the laureate
claimed that the virus was man-made and the result of “meticulous work” of
molecular biologists.

The second spike, occurring around late May - early June of 2021, followed an
interview of the same scientist in the French documentary “Hold-Up”, a
conspiratorial film which made numerous false claims about COVID-19. This
interview was released on May 13th 2021555https://planetes360.fr/pr-luc-
montagnier-les-variants-viennent-des-vaccinations/, and it was later given to
the RAIR (Rise Align Ignite Reclaim) foundation for an exclusive English
translation, published on May 18th
2021666https://rairfoundation.com/bombshell-nobel-prize-winner-reveals-covid-
vaccine-is-creating-variants/. The RAIR foundation describes itself as a
grassroots activist organization and an “integrated media platform amplifying
the voices of the silent majority”. It is a USA-based alternative media
platform which promotes mainly misinformation-laden, alt-right, and anti-
Muslim content. In this interview, the laureate claimed, among other things,
that vaccines are causing new coronavirus variants and that the vaccination
effort is an “unacceptable mistake”. This translation piece was the main
driver of the second spike.

From Figure 1b, which shows the number of tweets on a logarithmic scale, we
can observe that prior to COVID-19 the anti-consensus laureate mentions never
exceeded the total mentions for the pro-consensus laureates. After the sharp
drop in both pro-consensus and anti-consensus laureate mentions due to
COVID-19 not yet having picked up traction on Twitter by January 2020, there
is a rapid rise in anti tweets which is generally retained until the final
date for which data is available. The only notable exception is the Nobel
prize announcement of new laureates in October 2020, when the (newly
announced) pro-laureate mentions spike and briefly overtake anti-laureate
mentions.

In Figure 2 we also plot time-series frequency data for the vaccine dataset.
The pattern is comparable with what is seen in the general COVID-19 data
(Figure 1), since we also see more attention drawn to the anti-consensus
laureate around April 2020, then attention shifting to pro-consensus laureates
around October 2020, and then another shift to the anti-consensus laureate
following the RAIR article (Figure 2b). However, as expected for the vaccine
data, by far the most posts concern the anti-vaccine laureate’s statements
about the supposed dangers of the vaccination program in May 2021 (Figure 2a).

(a) Absolute values.

(b) Log-transformed values.

Figure 2: Frequency plots for vaccine dataset.

##  6 Popularity metrics

Our second research question concerned the drivers behind exposure to
different scientists on Twitter; in other words, whether exposure is driven by
the popularity of the accounts which amplify them, or if it is driven by the
sheer number of users who amplify those views.

###  6.1 Top posters

To get a clearer picture of how these views are disseminated on Twitter, we
first (anonymously) plot the top 20 users in terms of the number of anti-
consensus or pro-consensus tweets (and retweets) they produce, as well as the
top users in terms of their relative number of posts (Figure 3). To calculate
the relative number of posts, we simply count the number of tweets in our
sample produced by each user and divide them by the user’s total number of
tweets. We restrict this calculation to users who have posted at least 500
total tweets in order to preserve variation in the data. This allows for an
assessment of whether the absolute number of posts is due to a general
tendency to post more, or if some accounts disproportionately amplify these
Nobelists in the context of COVID-19. Because of the small sample in the
COVID-19 vaccine dataset, we only show the general COVID-19 plots here. For
context, the top anti user in terms of posting frequency for COVID-19 vaccines
had 47 posts, and the top pro user had just 7.

Users are indexed in descending order of the number of times they appear in
our dataset, meaning User0 is the top overall poster, User1 the second-top,
and so on. As is evident from the index numbers in Figure 3a, the anti-
consensus top posters are almost exclusively also the overall top posters.
Furthermore, the top 20 range is much higher for anti posters (approx. 50-800)
than pro posters (approx. 20-160). The top 20 anti posters post an average of
173.45 tweets, approximately 4.5 more than the top 20 pro (average 38.85
tweets). Notice that Users 4 and 5 appear in both the anti and pro top
posters. These are news aggregation accounts which collate and share news to
their audiences from across the platform in a fairly non-selective fashion.

Regarding the top 5 relative posters, we observed a suspicious pattern in the
accounts’ naming conventions which suggested they may be part of a botnet.
Four of these accounts are the most frequent anti-consensus posters. We
perform bot detection on these 5 accounts using the Botometer tool [14] which
uses several criteria, such as the account’s posting and deletion behavior,
whether the account has been labeled as a spambot in other datasets, whether
the account has been purchased as a follower bot, etc. Indeed, Botometer
assigns scores of 4.1, 3.8, 4.1, 4.2, and 3.8 out of 5 to these accounts
respectively, pointing to high bot-like activity. Average bot-like activity
for the 20 most active accounts is 2.5 for the relative posters, 3.05 for the
anti posters, and 2.88 for the pro posters. These are relatively high figures,
however this is expected as the accounts with the most posts are also more
likely to be the ones that automatically aggregate and share content on
Twitter.

(a) Anti posters.

(b) Pro posters.

(c) Relative posters.

Figure 3: Post frequencies of top 20 users per category.

###  6.2 Post reach

To assess post reach, we first examine whether there are any differences in
the popularity of the accounts which share pro-consensus and anti-consensus
tweets. We do this for both the general and vaccine-specific datasets, and we
report results both with retweets included and retweets removed. We report the
number of accounts sharing each type of tweet, along with their mean number of
followers, in Table 4. In case that an account has shared both pro- and anti-
consensus tweets, it is counted in both the pro and anti account calculations.
Generally, we find that the pro-consensus accounts tend to be much more
popular (i.e. have more followers), but are much fewer in numbers. Because of
the small number of accounts, and especially unique pro accounts in the
vaccine-specific dataset, we only analyze the general COVID-19 dataset in the
remainder of this section.

Dataset | With retweets | No retweets
---|---|---
|  | Followers | N | Followers | N
Vaccine | Anti | 5582.11 | 11559 | 23662.98 | 2025
| Pro | 10802.20 | 760 | 56476.43 | 89
General | Anti | 4003.49 | 83178 | 14679.94 | 15367
| Pro | 14767.46 | 58865 | 94267.30 | 7759
Table 4: Number of posting accounts with mean number of followers.

We also analyze whether anti and pro accounts differ in their verification
rates. Verified accounts on Twitter are those which have been identified by
the platform to be authentic and of public interest. The criteria Twitter uses
to grant verification badges to accounts are that the account must be
“authentic, notable, and active”.777Twitter: About verified accounts. To
compare verification rates, we filter out all retweets and retain only unique
original authors. We then perform a chi-square test of association which is
significant, $\chi^{2}(1)=945.07,\textit{p}<.001$. Table 5 is the contingency
table used in this test. This suggests that pro-consensus accounts are
significantly more likely to be verified than anti-consensus accounts.

| Anti | Pro
---|---|---
| Observed | Expected | Observed | Expected
Unverified | 15055 | 14562.97 | 6861 | 7353.93
Verified | 312 | 804.03 | 898 | 405.97
Table 5: Contingency table for determining association between verification
status and consensus stance.

Next, we analyze popularity at the tweet level. Our goal is to see whether
pro-consensus tweets tend to get more retweets and likes, and whether this is
commensurate with the followership of the accounts which post them. For this
purpose, we once again filter out retweets. We do this because of the retweet
object type in Twitter’s API, which assigns to retweets the same number of
retweets as the original tweet. For example, if one tweet has 1000 retweets,
then each of these retweets will also appear to have 1000 retweets of its own.
Therefore, we ignore retweets to avoid inflating popularity metrics.

At the tweet level, we examine five key metrics: number of likes, number of
retweets, number of followers of posting account, follower-weighted likes
($\frac{likes+1}{followers+1}$) and follower-weighted retweets
($\frac{retweets+1}{followers+1}$). The distribution of all metrics is heavily
right-skewed mainly due to the presence of many zero values ($\textit{p}<.001$
in Shapiro-Wilks tests for all variables of interest). For this reason, we
utilize the Mann-Whitney U test which makes no parametric assumptions, for
pairwise comparisons between anti and pro tweets. The Mann-Whitney U test
ranks values in an ordinal fashion from lowest to highest and performs
comparisons based on this rank. We report the results of these tests in Table
6.

| Anti | Pro |  |  |
---|---|---|---|---|---
| Mean rank | N | Mean rank | N | U | Z | p
Likes | 17050.88 | 25683 | 19321.71 | 9659 | 108096706.0 | 20.85 | < .001
Retweets (RTs) | 17142.18 | 25683 | 19078.93 | 9659 | 110441637.5 | 20.41 | < .001
Followers | 16202.31 | 25683 | 21578.02 | 9659 | 86302965.5 | 44.15 | < .001
Likes per follower | 19024.54 | 25683 | 14073.79 | 9659 | 89285802.5 | 40.66 | < .001
RTs per follower | 19133.76 | 25683 | 13783.40 | 9659 | 86480900.5 | 43.94 | < .001
Table 6: Results of Mann Whitney U tests. Bonferroni corrections are applied.

A higher mean rank means the values in that group are also higher overall. As
is seen in Table 6, pro tweets receive significantly higher likes and retweets
than anti tweets and are posted by accounts with significantly higher numbers
of followers. However, when we look at popularity metrics relative to the
number of followers of the posting accounts, the opposite is true. Anti posts
receive more likes and retweets per follower than pro posts. This suggests
that, while pro posters are generally more popular on Twitter, anti tweets
garner more relative attention and drive higher engagement.

##  7 Social network analysis

Our third and final research question concerned the topology of the network
which propagates pro-consensus and anti-consensus information across the
Twitter platform. We constructed a weighted retweet network graph and
visualized it using the ForceAtlas2 algorithm which clusters nodes based on
how well-connected they are [22]. We applied the Louvain algorithm which is a
modularity optimization method for community detection [10], and colorized
distinct communities based on their modularity class. We performed these tasks
using the Gephi software [7].

###  7.1 General COVID-19 network visualization

We first analyze the network graph for the general COVID-19 dataset. We filter
out any nodes which are not connected to the giant component (i.e. nodes which
do not have any connections to the main network). With this filtering, the
network consists of 159K edges and 129K nodes. This retweet network
visualization is seen in Figure 4, with the 10 largest communities labelled
from biggest (1) to smallest (10). We scale nodes based on in-degree.

Figure 4: Retweet graph visualization for general COVID-19 dataset.

##### First cluster.

The largest (purple) community contains 14.08% of all network nodes. This
community is centered around the official Nobel prize account, which has by
far the highest in-degree (18546) and highest authority (0.9999682) in the
entire network. The round shape of this community indicates that this central
Nobel prize account is almost exclusively retweeted in this community (i.e.,
there is no information propagation between other nodes). Its size also
possibly stems from the traction that the official Nobel prize announcements
receive. Generally, this community almost exclusively discusses pro-consensus
laureates. Some other scientific accounts, such as the official Nature
magazine account, also form part of this cluster.

##### Second cluster.

The second-largest (green community) contains 12.25% of the nodes. It is a
French-based misinformation cluster which disproportionately mentions the
anti-consensus laureate. This community seems to bridge the entire network,
since it contains the top two accounts in terms of betweenness centrality
(126387.67 and 89829.35). CNEWS, mentioned above as a key vehicle through
which this laureate gained exposure, appears in this community. It has the
third-highest in-degree in the entire network (4206). Accounts in this cluster
are generally very prone to spreading misinformation.

##### Third cluster.

Next in terms of size is a Brazilian misinformation community (blue), which
includes 6.89% of network nodes. Compared to the French misinformation
cluster, the nodes here have fewer in-degrees but there are more “central
players”. The entire community seems to be centered around an account which
comes across as a news site, but shares unverified news.

##### Fourth cluster.

The fourth-biggest community (yellow, 5.69% of nodes) is built around English-
speaking mainstream media. Examples of accounts appearing here are the BBC’s
Question Time (which often features scientists), and associated journalists.
Fact-checking accounts such as FullFact also appear here, but are not central
in the cluster.

##### Fifth cluster.

The orange cluster (5.32% of network nodes) is an English-speaking
misinformation community. It contains several roughly equally-retweeted nodes
with high in-degrees (highest being 1140). Most accounts here systematically
and frequently spread misinformation and COVID-19 conspiracy theories, with a
few accounts seemingly dedicated to COVID-19 specifically. A key node in this
cluster is the account of a RAIR foundation journalist, possibly due to their
role in popularizing the anti-consensus laureate’s anti-vaccination
sentiments. This cluster also contains several accounts high in betweenness
centrality, placing this cluster, along with the French misinformation
cluster, in the middle of the entire network. These two clusters also
constitute the most important misinformation communities in the network.

##### Sixth cluster.

Another important community in the network is a Japanese misinformation
cluster (reddish-pink, 4.72% of nodes). Nearly all of the network traffic
concerns two accounts belonging to a book author and an academic (who has the
fourth-highest in-degree in the network; 3572). These accounts tweet both in
Japanese and in English, and they mostly quote the anti-consensus laureate on
his claims that the virus was engineered in a lab. These accounts seem to be
driven by a motivation to challenge China and call for accountability on its
part. They seem to use misinformation as a means of promoting this anti-China
sentiment.

##### Seventh cluster.

Next, shown in beige-pink in the visualization, is an Argentine misinformation
community which contains 4.32% of the nodes found in the network. This is a
more loosely-connected community with no central players, however there do
seem to be some accounts dedicated specifically to opposing the official story
on COVID-19.

##### Eighth cluster.

We also observe another English-speaking misinformation community, which
seems, however, to be based in India (light brown, 4.27%). Some accounts in
this cluster exhibit anti-Muslim sentiment, however others do not seem to be
systematic misinformers. Rather, some accounts in this cluster shared the
anti-consensus laureate’s statements on a few occasions and drew a lot of
retweets as a result.

##### Ninth cluster.

In light-blue is a Turkish misinformation community (4.11% of network nodes).
The cluster seems to be centered around two journalist-like accounts, one of
which has the fifth-highest in-degree in the network (3553). These accounts
behave like news aggregators and do not seem to misinform on purpose. However,
the majority of their traffic does stem from tweets concerning the anti-
consensus laureate’s statements, which were shared widely within the cluster.

##### Tenth cluster.

Finally, in red is an unrelated Japanese community containing 3.64% of all
network nodes. There is only one central node in this cluster which also
happens to be the second-highest in terms of in-degree (4715). This account
belongs to a Japanese boy-band whose members hosted a podcast episode with one
of the pro-consensus laureates. Nearly all of the traffic in this network
comes from a single tweet which advertized this episode. As seen from Figure
4, this community is quite separated from the rest of the network, albeit
still loosely connected. This is likely due to the distinct audience that this
account attracts, which is likely more interested in pop culture rather than
the pandemic and surrounding policies.

##### Bot network locations.

We are also able to trace the top 5 relative posters that were identified as
potential bots. We use the top relative poster (User1) as a focal node and
filter its ego network at a depth of 1 (i.e., accounts which have directly
retweeted it). We find that two of the remaining four accounts in the top 5
appear in this ego network (Users 0 and 2). When we extend the network depth
to 3, we find that this botnet is situated between the French and English
misinformation clusters (green and orange), with relatively close ties to the
Brazilian misinformation cluster as well. This suggests that information may
propagate through this botnet across these three communities, although no
nodes in this botnet show high centrality or authority metrics. With regards
to the second-highest relative user (User8), they are completely absent from
the giant component of the network and thus inconsequential here. The fifth-
highest relative user (User3) is situated within the Argentine misinformation
cluster. It should be noted that the four nodes which are part of the giant
component all belong to the same community (Argentine misinformation cluster),
suggesting that they do retweet each other, but also amplify different types
of accounts.

###  7.2 COVID-19 vaccine network visualization.

We also visualize the retweet network graph for the vaccine dataset in Figure
5, where the formation of communities is more straightforward. We scale node
size based on its in-degree. The general pattern we observe is that most
communities are centered around a single focal user, although some communities
are more diverse in terms of their most retweeted users. Again, we filter out
any nodes which are not connected to the giant component, which leaves 9.8K
nodes and 10.7K edges in the network. As with the general COVID-19 network, we
label communities from 1 (biggest) to 10 (10th biggest). We note that the 9th
biggest community is absent from the graph visualization because it is not
connected to the giant component.

Figure 5: Retweet graph visualization for COVID-19 vaccine dataset.

##### Anti-consensus communities with focal centers.

The two largest communities (red-brown (1), 8.51% of nodes; light blue (2),
8.17% of nodes) are centered around the RAIR foundation journalist discussed
above and an account which tweets exclusively about COVID-19 conspiracy
theories, respectively. Both of these accounts appear in the English-speaking
misinformation cluster of the general COVID-19 dataset (in orange, Figure 4).
These two nodes are also the most important in the entire network. The
journalist has the highest betweenness centrality out of all nodes (4028.5)
and the second-highest authority (0.302) and in-degree (970). On the other
hand, the conspiracy account has the highest authority (0.951) and in-degree
(1026) in the network.

Communities 4-7 also follow similar trends. Community 4 (6.66%) is centered
around a misinformation account with the third-highest in-degree in the
network (833). This account appears in the French misinformation cluster in
the general COVID-19 network (in green, Figure 4). The fifth community (5.13%)
is centered around an account which similarly appears in the English-speaking
misinformation community in the previous section. This account has the fourth-
highest in-degree in the network (650). Communities 6 (4.95%) and 7 (3.61%)
are also centered around two distinct nodes. These concern Japanese accounts
which are prone to misinformation, although they are not the same accounts
which drive the traffic of the Japanese misinformation cluster in Figure 4.

##### Decentralized anti-consensus communities.

Communities 8 (3.6%) and 10 (2.6%) also tend to amplify the anti-consensus
laureate and generally spread misinformation, although they do not feature
nodes which are as central as the previously discussed communities. Community
8 tends to cross-tweet with community 2. It features both Chinese and English-
speaking accounts, making it a fairly suspicious cluster. The most retweeted
account here belongs to an independent Australian Member of Parliament who has
shared many COVID-related conspiracy theories and amplified the anti-consensus
laureate on several occasions. Community 10 is adjacent to community 4 as it
is also a French-speaking, albeit less centralized, misinformation community
which also amplifies the anti-consensus laureate.

##### Non-misinformation communities.

The third largest cluster (3) (containing 7.15% of all nodes) belongs to a
network of India-based fact-checkers who predominantly tweet in English. Many
of the most retweeted accounts in this community belong to members of an
independent Indian organization dedicated to debunking online falsehoods.
Taking this into account, it is possible that the high betweenness centrality
of the RAIR foundation journalist stems from the fact that they are connected
both to all misinformation clusters, but also to this fact-checking group
which targeted their anti-vaccine piece on the anti-consensus laureate.

The ninth-biggest community (3.06% of nodes) also seems to be fairly pro-
science, featuring the official Nobel prize account and medical doctors, among
others (although we do note that some medical doctors are featured in
misinformation clusters where they are seen amplifying the anti-consensus
laureate). However, this community is completely separate from the giant
component of the network and therefore does not appear in Figure 5.

##  8 Discussion

In this work, we examined whether consensus-dissenting scientists are
disproportionately amplified on Twitter such that a false consensus effect is
created around COVID-19.

###  8.1 Summary of findings

We used a proxy for scientific consensus consisting of Nobelists in Physiology
or Medicine. Using this method, we derived the scientific consensus ratio of
anti-vaccine to pro-vaccine scientists as 1:24. We expanded this ratio to
broader COVID-19 topics as well, beyond just vaccines.

####  8.1.1 False consensus trends

Our results showed that between January 2020 and July 2021, the anti-consensus
(minority) scientist was amplified by a factor of 426.05 relative to the
scientific consensus ratio in the context of COVID-19 vaccines, and by a
factor of 42.9 in the context of COVID-19 in general. These findings cannot be
explained by the previous popularity of consensus-abiding or dissenting
scientists, since baseline data collected prior to COVID-19 becoming a
trending topic suggested that Twitter conversations roughly followed the
scientific consensus ratio. In other words, we observed a strong false
consensus effect on the Twitter platform in COVID-19-related topics and
especially on COVID-19 vaccines.

Time-series analyses revealed that mentions of the laureates we used in our
ratio are predominantly event-driven. The consensus-abiding laureates received
the most mentions around the time of their announcement as Nobel prize
winners. However, the dissenting laureate received the most attention on two
occasions where they made consensus-opposing claims to mainstream and
alternative media. Whereas we were able to find several occasions where other
laureates made media statements during our consensus categorization exercise
(Appendix A), there were no commensurate spikes following these appearances.
This suggests that misinformation-laden yet scientific-sounding information is
uniquely equipped to propagate through social media, consistent with the
findings of [30] on the believability of “scientific” misinformation.

####  8.1.2 Popularity analyses

We also examined the drivers of this false consensus on Twitter from a
popularity and engagement perspective. We found that the top 20 anti-consensus
posters produce almost 4.5 times as much content as the top 20 pro-consensus
posters. However, we also found that on average, top posters in both
categories exhibited quite high bot-like activity (as expected, since a lot of
these may be aggregator bots). In terms of potential for reach, we found that
pro-consensus accounts were generally more likely to be verified, and that
they amassed larger followerships than anti-accounts. In addition, pro-
consensus tweets did receive more likes and retweets than anti-consensus
tweets. However, when normalizing engagement by the posting accounts’
potential for reach (number of followers), we found that anti-consensus posts
received higher relative engagement (i.e., more likes and retweets per
follower). This suggests that the dissenting scientist mainly receives
exposure through higher engagement and more relative propagation, rather than
due to being featured on more popular accounts than the consensus-abiding
scientists.

####  8.1.3 Retweet network analyses

We conducted network analyses on both COVID-19 vaccines specifically and
COVID-19 in general. For COVID-19 more generally, we were able to find that
among the 10 largest communities, 7 communities are misinformation prone.
These communities span multiple regions, namely Japan, Brazil, Argentina,
Turkey, France, India, and English-speaking countries.

Contrary to Singh et al. [44], we did not find that mainstream news sources
are centrally located in the network. Rather, the main brokerage role seems to
fall with the French misinformation cluster which also includes the nodes with
the highest betweenness centralities in the entire network. The English-
speaking misinformation cluster is also relatively quite central. We believe
the difference between our results and [44] stems from the fact that Singh et
al. relied on a URL domain approach to collect and categorize misinformation
and reliable news. Since we used names of featured scientists to do this, our
results may suggest that mainstream media do not feature consensus-abiding
scientists or do not receive enough exposure when they do so in order to
effectively intercept such a network. However, this would need to be verified
with a wider set of scientists making up the scientific consensus.

With respect to the vaccine retweet network, the dynamics change slightly.
Once again, this network is dominated by misinformation-prone clusters which
amplify the dissenting scientist (8 out of the 10 largest communities).
However, communities tend to be structured around a central node which is
usually the community “leader” in terms of in-degrees. This is the case for
all but two of the false consensus-driving communities.

Strangely, we also observe that the ninth-largest community, featuring mainly
pro-consensus accounts, is fully disconnected from the main component. In the
context of COVID-19 vaccines, this suggests that the misinformation
communities do not receive any exposure to the true scientific consensus from
this community whatsoever. Nonetheless, we find that the third largest
community in the vaccine network is dedicated to fact-checking mainly the
largest misinformation community, placing the latter in the middle of the
entire network in terms of betweenness centrality.

###  8.2 Implications

This work carries several insights for practice. To our knowledge, it is the
first paper to examine false consensus biases in the context of COVID-19. It
is also the first paper to study deviations between public perception and
scientific consensus using social media data. Previously, Harvey [20] was able
to demonstrate that approximately 80% of the 90 climate change-denying blogs
in their sample cited the same professor as the main source for their claims.
Here we show a similar pattern regarding the topic of COVID-19, since the one
scientist who dissented from scientific consensus in our ratio was also the
one who was disproportionately amplified. This is important because prior
research suggests that the average person is fixated on the number of
secondary, and not primary sources for a claim to assess its credibility [51].
In other words, even though the dissenting scientist (primary source in our
case) was a very small minority, the fact that multiple secondary sources
(other accounts) cited them is likely to have created the illusion that their
view is much more established in the scientific community than it really is.
This may be especially true for misinformation-prone, homogeneous groups which
already hold anti-vaccination or otherwise conspiratorial sentiments [48].

Overall, we find that dissenting scientific voices are amplified to a much
larger extent than consensus-abiding scientific voices. We thus identify
dissenting scientific voices as a prominent source of COVID-19 misinformation
on Twitter. Regarding social media governance around misinformation, a
worthwhile avenue of controlling the spread of false information particularly
around COVID-19 is an attempt to highlight the voices of consensus-abiding
scientists. Especially because secondary sources can play a major role in
shaping the informational ecosystem [51], balancing out the amplification of
dissenting voices with commensurate exposure to scientists who subscribe to
true consensus may serve to at least reduce the rate at which scientific-
sounding misinformation propagates.

Consistent with findings around the relative propagation of true and false
information on Twitter [46] we also find that consensus-dissenting tweets
receive higher relative engagement compared to consensus-abiding tweets, even
though the latter tend to be posted by more recognized and popular accounts.
It is likely that consensus-dissenting opinions, even when stemming from
scientists, may serve other purposes such as legitimizing anti-establishment
sentiments for the user amplifying them [17, 35, 37]. Therefore, a worthwhile
avenue of further combating misinformation may be addressing the dissenting
voices head-on instead of simply promoting content from scientists subscribing
to scientific consensus in a disassociated manner. This could be in the form
of consulting consensus-abiding voices to “fact-check” consensus-dissenting
voices, such that the normalization of relative exposure (and thus the
reduction of false consensus) becomes inherent to the mechanisms via which
scientific disagreement takes place on social media.

With respect to our social network analyses, we find that events which grant
exposure to consensus-dissenting scientists are propagated internationally. We
only observe spikes in mentions around the anti-consensus laureate
periodically (Figure 1) following media appearances, however these events are
discussed on Twitter across multiple countries. This means that scientist-
backed misinformation, even when produced locally in one country as was the
case with the anti-consensus laureate in question, can have wide-reaching
resonance. To this end, early detection and control of disproportionate
amplification can be impactful. However, a temporal analysis of the retweet
network development could provide additional insights as to exactly how useful
such an approach would be.

###  8.3 Limitations and future research directions

The present work does suffer from some limitations. First and foremost, our
proxy method for determining scientific consensus was quite restricted.
Although there are thousands of scientists working on various aspects of the
COVID-19 pandemic, we used only 25 people based on our selection criteria to
form a ratio of scientific consensus. It is very likely that the false
consensus factors we report here would vastly change based on a different,
more extensive ratio. We therefore call on future research to either collate
information on true scientific consensus around the COVID-19 pandemic (much
how [13] did for scientific consensus on climate change), or to utilize
exploratory computational methods to derive this based on named-entity
recognition.

Furthermore, we based our tweet classification approach on a fairly
unsophisticated heuristic rule. Future research could utilize natural language
processing models such as BERT [15] to more accurately classify tweets into
anti- or pro-consensus, due to instances of the heuristic rule failing as
described in §3. This would also assist with better context recognition to
prevent the introduction of synonymy-induced noise (i.e., identifying whether
a tweet is truly talking about a specific Nobel laureate and not another
person with the same name). However, future research could also utilize
similar approaches in order to detect instances where broader COVID-19
misinformation on Twitter and other social media platforms invokes the opinion
of scientifically credible figures.

Finally, this paper reports on an exploratory analysis of the false consensus
information ecosystem on COVID-19 on Twitter. For this reason, we do not go
into depth regarding the exact linguistic attributes of tweets which cite
consensus-abiding or consensus-dissenting scientists. Such an analysis in
future research would be promising, as it could reveal textual features which
may be driving the increased engagement of anti-consensus tweets relative to
the pro-consensus tweets.

###  8.4 Conclusion

In this study, we operationalized false consensus as the deviation between
public consensus on Twitter and “true” scientific consensus as derived through
our proxy method. We are able to answer our research questions as follows.

  1. RQ1:

Dissenting scientists are indeed amplified to a large extent relative to
consensus-abiding scientists. Specifically, we observe a false consensus
factor of 426.05 in the context of COVID-19 vaccines, and a factor of 42.9 in
the context of COVID-19 more generally.

  2. RQ2:

Consensus-dissenting tweets seem to drive higher engagement than consensus-
abiding tweets. Specifically, we find that pro-consensus tweets tend to be
produced by more popular and recognized accounts. However, relative to their
potential for reach, anti-consensus tweets receive more likes and retweets per
potential user (follower) who could be exposed to them.

  3. RQ3:

For COVID-19 in general, consensus-dissenting tweets propagate across the
platform to reach audiences in several different countries. Anti-consensus
clusters are more centrally located than pro-consensus clusters, which tend to
be more mainstream, yet more marginalized in this specific context. With
respect to COVID-19 vaccines, clusters are predominantly centered around focal
users whose amplifications of consensus-dissenting scientists receive a lot of
engagement. We do find some attempts to revert this as a certain community
targets these high-engagement tweets with debunking and fact-checking efforts.

We hope that this research can inform social media governance and response to
misinformation, as well as highlighting the use of credible messengers [16] as
a method of debunking scientific-sounding falsehoods and balancing out false
consensus.

##  9 Acknowledgments

This research has been partially funded by the UK EPSRC grant EP/S022503/1
which supports the UCL Centre for Doctoral Training in Cybersecurity. Any
opinions, conclusions, or recommendations expressed in this work are those of
the authors and do not necessarily reflect the views of the UK EPSRC.

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##  Appendix A Laureates included in ratio

Note that some of these laureates have expressed their opinions on more than
one occasion, however we only give one source per laureate here. We did not
find any laureates who expressed conflicting opinions on separate occasions.

Laureate |  Year awarded |  Vaccine stance |  Source |  Source description
---|---|---|---|---
Eric Kandel |  2000 |  Pro |  Open letter |  Signs open letter to expand vaccine access
Leland Hartwell |  2001 |  Pro |  Open letter |  Signs open letter to expand vaccine access
Paul Nurse |  2001 |  Pro |  Statement |  Explains importance of vaccination
Tim hunt |  2001 |  Pro |  Open letter |  Signs open letter to expand vaccine access
Robert Horvitz |  2002 |  Pro |  Presidential advice |  Commends the efforts and speed in developing vaccines
Barry Marshall |  2005 |  Pro |  Projection |  Predicts the vaccine manufacturing process
Andrew Fire |  2006 |  Pro |  Interview |  Promoted open science on vaccine technology
Robert Mello |  2006 |  Pro |  Interview |  Describes mRNA vaccine technology as safe
Mario Capecchi |  2007 |  Pro |  Open letter |  Signs open letter to expand vaccine access
Francoise Barre-Sinoussi |  2008 |  Pro |  Open letter |  Signs open letter to expand vaccine access
Elizabeth Blackburn |  2009 |  Pro |  Open letter |  Signs open letter to expand vaccine access
Jules Hoffmann |  2011 |  Pro |  Public appearance |  Appears taking the vaccine and describes it as safe
Shinya Yamanaka |  2012 |  Pro |  Interview |  Calls on the Japanese government to speed up vaccinations
Randy Schekman |  2013 |  Pro |  Webinar |  Urges the public to follow official advice on vaccinations
John O’Keefe |  2014 |  Pro |  Open letter |  Signs open letter to expand vaccine access
May-Britt Moser |  2014 |  Pro |  Open letter |  Signs open letter to expand vaccine access
Edvard Moser |  2014 |  Pro |  Interview |  Commends early science which enabled quick vaccine development
Jeffrey Hall |  2017 |  Pro |  Open letter |  Signs open letter to expand vaccine access
Michael Rosbash |  2017 |  Pro |  Interview |  States that the quick development of a vaccine is crucial against COVID-19
James Allison |  2018 |  Pro |  Public appearance |  Appears taking the vaccine and declares “proud to stand with science”
Tasuku Honjo |  2018 |  Pro |  Open letter |  Signs open letter to expand vaccine access
Gregg Semenza |  2019 |  Pro |  Open letter |  Signs open letter to expand vaccine access
Michael Houghton |  2020 |  Pro |  Interview |  Urges people to take a vaccine as soon as it is available
Charles Rice |  2020 |  Pro |  Projection |  Forecasts the quick development of vaccines
Luc Montagnier |  2008 |  Anti |  Interview |  Calls mass vaccination an “unacceptable mistake”

##  Appendix B Search query

Covid synonyms | Vaccine synonyms | Laureate names |
---|---|---|---
covid | vaxx | Francoise Barre-Sinoussi | Randy Schekman
covid-19 | vaccination | Eric Kandel | John O’Keefe
coronavirus | vaccine | Leland Hartwell | May-Britt Moser
rona | vac | Paul Nurse | Edvard Moser
pandemic | vaccinated | Tim Hunt | Michael Rosbash
plandemic | jab | Robert Horvitz | James Allison
virus | vax | Barry Marshall | Tasuku Honjo
lockdown | anti-vax | Andrew Fire | Gregg Semenza
corona | vaxxer | Robert Mello | Michael Houghton
| anti-vaxxer | Mario Capecchi | Charles Rice
| vaxer | Elizabeth Blackburn | Luc Montagnier
| anti-vaxer | Jules Hoffmann | Jeffrey Hall
|  | Shinya Yamanaka |

Laureate names were enclosed in double quotations. OR statements were applied
down columns, AND statements were applied across columns. The Twitter API is
not case-sensitive and dashes also return condensed versions (for example,
covid-19 also returns covid19, and covid also returns COVID, Covid, etc).

For the generic COVID dataset, the “vaccine” column was ejected. For the
baseline dataset, both the “covid” and “vaccine” columns were ejected.