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Bots and Misinformation Spread on Social Media:Implications for
COVID-19
McKenzie Himelein-Wachowiak1, BA; Salvatore Giorgi1,2, MSc; Amanda Devoto1, PhD; Muhammad Rahman1, PhD;
Lyle Ungar2, PhD; H Andrew Schwartz3, PhD; David H Epstein1, PhD; Lorenzo Leggio1, MD, PhD; Brenda Curtis1,
MSPH, PhD
1Intramural Research Program, National Institute on Drug Abuse, Baltimore, MD, United States
2Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States
3Department of Computer Science, Stony Brook Unversity, Stony Brook, NY, United States
Corresponding Author:
Brenda Curtis, MSPH, PhD
Intramural Research Program
National Institute on Drug Abuse
251 Bayview Blvd
Suite 200
Baltimore, MD, 21224
United States
Phone: 1 443 740 2126
Email: brenda.curtis@nih.gov
Abstract
As of March 2021, the SARS-CoV-2 virus has been responsible for over 115 million cases of COVID-19 worldwide, resulting
in over 2.5 million deaths. As the virus spread exponentially, so did its media coverage, resulting in a proliferation of conflicting
information on social media platforms—a so-called “infodemic.” In this viewpoint, we survey past literature investigating the
role of automated accounts, or “bots,” in spreading such misinformation, drawing connections to the COVID-19 pandemic. We
also review strategies used by bots to spread (mis)information and examine the potential origins of bots. We conclude by conducting
and presenting a secondary analysis of data sets of known bots in which we find that up to 66% of bots are discussing COVID-19.
The proliferation of COVID-19 (mis)information by bots, coupled with human susceptibility to believing and sharing
misinformation, may well impact the course of the pandemic.
(J Med Internet Res 2021;23(5):e26933) doi: 10.2196/26933
KEYWORDS
COVID-19; coronavirus; social media; bots; infodemiology; infoveillance; social listening; infodemic; spambots; misinformation;
disinformation; fake news; online communities; Twitter; public health
Introduction
Globally, 2020 has been characterized by COVID-19, the
disease caused by the SARS-CoV-2 virus. As of March 2021,
the COVID-19 pandemic has been responsible for over 115
million documented cases, resulting in over 2.5 million deaths.
The United States accounts for 24.9% of the world’s COVID-19
cases, more than any other country [1].
As the virus spread across the United States, media coverage
and information from online sources grew along with it [2].
Among Americans, 72% report using an online news source
for COVID-19 information in the last week, with 47% reporting
that the source was social media [3]. The number of research
articles focusing on COVID-19 has also grown exponentially;
more research articles about the disease were published in the
first 4 months of the COVID-19 pandemic than throughout the
entirety of the severe acute respiratory syndrome (SARS) and
Middle East respiratory syndrome (MERS) pandemics combined
[4]. Unfortunately, this breadth, and the speed with which
information can travel, sets the stage for the rapid transmission
of misinformation, conspiracy theories, and “fake news” about
the pandemic [5]. One study found that 33% of people in the
United States report having seen “a lot” or “a great deal” of
false or misleading information about the virus on social media
[3]. Dr Tedros Adhanom Ghebreyesus, the Director-General of
the World Health Organization, referred to this accelerated flow
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of information about COVID-19, much of it inaccurate, as an
“infodemic” [6].
Though the pandemic is ongoing, evidence is emerging
regarding COVID-19 misinformation on social media. Rumors
have spread about the origin of the virus, potential treatments
or protections, and the severity and prevalence of the disease.
In one sample of tweets related to COVID-19, 24.8% of tweets
included misinformation and 17.4% included unverifiable
information [7]. The authors found no difference in engagement
patterns with misinformation and verified information,
suggesting that myths about the virus reach as many people on
Twitter as truths. A similar study demonstrated that fully false
claims about the virus propagated more rapidly and were more
frequently liked than partially false claims. Tweets containing
false claims also had less tentative language than valid claims
[8].
This trend of misinformation emerging during times of
humanitarian crises and propagating via social media platforms
is not new. Previous research has documented the spread of
misinformation, rumors, and conspiracies on social media in
the aftermath of the 2010 Haiti earthquake [9], the 2012 Sandy
Hook Elementary School shooting [10], Hurricane Sandy in
2012 [11], the 2013 Boston Marathon bombings [12,13], and
the 2013 Ebola outbreak [14].
Misinformation can be spread directly by humans, as well as
by automated online accounts, colloquially called “bots.” Social
bots, which pose as real (human) users on platforms such as
Twitter, use behaviors like excessive posting, early and frequent
retweeting of emerging news, and tagging or mentioning
influential figures in the hope they will spread the content to
their thousands of followers [15]. Bots have been found to
disproportionately contribute to Twitter conversations on
controversial political and public health matters, although there
is less evidence they are biased toward one “side” of these issues
[16-18].
This paper combines a scoping review with an unpublished
secondary analysis, similar in style to Leggio et al [19] and Zhu
et al [20]. We begin with a high-level survey of the current bot
literature: how bots are defined, what technical features
distinguish bots, and the detection of bots using machine
learning methods. We also examine how bots spread
information, including misinformation, and explore the potential
consequences with respect to the COVID-19 pandemic. Finally,
we analyze and present the extent to which known bots are
publishing COVID-19–related content.
What Are Bots?
Before addressing issues surrounding the spread of
misinformation, we provide a definition of bots, describe their
typical features, and explain how detection algorithms identify
bots.
Definition and Identification
Bots, shorthand for “software robots,” come in a large variety
of forms. Bots are typically automated in some fashion, either
fully automated or human-in-the-loop. There is a common
conception that all bots spam or spread malware, but this is not
the case. Some bots are benign, like the Twitter account
@big_ben_clock, which impersonates the real Big Ben clock
by tweeting the time every hour [21]. Others have even been
used for social good, such as Botivist, which is a Twitter bot
platform used for recruiting volunteers and donations [22].
Groups of bots can function in coordination with each other,
forming what are called botnets [23]. One botnet of roughly
13,000 bot accounts was observed tweeting about Brexit, with
most of these bot accounts disappearing from Twitter shortly
after the vote [24]. Bots of all types are ubiquitous on social
media and have been studied on Reddit [25,26], Facebook [27],
YouTube [28], and Twitter [29], among other platforms.
Given their large variety, bots are often organized into
subclasses, a selection of which we discuss here. Content
polluters are one subclass; these are “accounts that disseminate
malware and unsolicited content.” Traditional spambots, another
subclass, are “designed to be recognizable as bots” [30]. Social
bots—a newer, more advanced type of bot [31-33]—use “a
computer algorithm that automatically produces content and
interacts with humans on social media, trying to emulate and
possibly alter their behavior.” There are also hybrid human-bot
accounts (often called cyborgs) [34], which “exhibit human-like
behavior and messages through loosely structured, generic,
automated messages and from borrowed content copied from
other sources” [35]. It is not always clear which category a bot
may fall into (eg, if a given social bot is also a cyborg).
Various methods have been used to identify bots “in the wild,”
so as to build the data sets of known bots used to train
bot-detection algorithms. One method, the “social honeypot”
[36], mimics methods traditionally used by researchers to
monitor hacker activity [37] and email harvesting [38].
Specifically, social honeypots are fake social media profiles set
up with characteristics desirable to spammers, such as certain
demographics, relationship statuses, and profile pictures [39].
When bots attempt to spam the honeypots (by linking
malware-infested content or pushing product websites),
researchers can easily identify them.
Technical Features of Bots
Overview
Features that distinguish bots from humans roughly fall into
three categories: (1) network properties, such as hashtags and
friend/follower connections, (2) account activity and temporal
patterns, and (3) profile and tweet content. These feature
categories have the advantage of being applicable across
different social media platforms [27].
Network Properties
Networks based on friend/follower connections, hashtag use,
retweets, and mentions have been used in a number of studies
that seek to identify social bots [40-43], exploiting network
homophily (ie, humans tend to follow other humans and bots
tend to follow other bots). As bots become more sophisticated,
network properties become less indicative of them; studies have
found groups of bots that were able to build social networks
that mimic those of humans [44].
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Account Activity and Temporal Patterns
Patterns of content generation can be good markers of bots.
Bots compose fewer original tweets than humans, but retweet
others’ tweets much more frequently, and have a shorter time
interval between tweets [40]. Ferrara et al [31] found that
humans are retweeted by others more than are bots, suggesting
that bots may struggle to compose convincing or interesting
tweets. However, many others have found this not to be the case
[15,16,33]. Finally, humans typically modify their behavior
during each online session; as the session progresses, the density
of new tweets decreases. Bots do not engage in these “sessions”
of social media usage, and accordingly do not modify their
behavior [45].
Profile and Tweet Content
Profile metadata such as account age and username can be used
to identify social bots. Ferrara et al [31] showed that bots have
shorter account age (ie, the accounts were created more
recently), as well as longer usernames. Automatic sentiment
analysis of tweet content has also been studied as a means of
distinguishing bots from humans. One study found humans
expressed stronger positive sentiment than bots, and that humans
more frequently “flip-flopped” in their sentiment [42].
Detection of Bots
Over the past decade, several teams have sought to develop
algorithms that successfully identify bots online. Social media
platforms use similar algorithms internally to remove accounts
likely to be bots. These algorithms originated in early attempts
to identify spam emails [46], social phishing [47], and other
types of cybercrimes [37]. With the advent of online
communities, cybercriminals turned their attention to these sites,
eventually creating fake, automated accounts at scale [48]. Table
1provides a summary of several prominent papers on bot
identification. We note that the details of specific machine
learning algorithms are beyond the scope of this paper and
therefore are not included in this manuscript.
Table 1. Review of state-of-the-art detection of bots on Facebook and Twitter.
Predictive accuracyMetricModelFeaturesNumber of accountsPlatformType and reference
Cc
Tb
Na
Human judgment (manual annotation)
0.57F1-scoreManual annotation928TwitterCresci et al (2017) [33]
Automatic methods
0.96 (Facebook),
0.99 (Twitter)
Detection rateNaïve Bayes, decision
trees, rule learners
✓320 (Facebook), 305
(Twitter)
Facebook
and Twitter
Ahmed and Abulaish
(2013) [27]
0.73Area under the
curve
Gradient boosting✓✓✓897TwitterDickerson et al (2014) [42]
0.92F1-scoreDigital DNA se-
quences
✓928TwitterCresci et al (2017) [33]
0.95Area under the
curve
Random forests✓✓✓21,000TwitterVarol et al (2017) [41]
>0.99Area under the
curve
AdaBoost✓8386TwitterKudugunta and Ferrara
(2018) [49]
0.87F1-scoreLong short-term
memory networks
✓1000TwitterMazza et al (2019) [50]
0.72F1-scoreSupport vector ma-
chines, decision trees,
Naïve Bayes
✓1000FacebookSantia et al (2019) [51]
0.60-0.99Area under the
curve
Random forests✓✓137,520TwitterYang et al (2020) [52]
aN: network properties.
bT: account activity and temporal patterns.
cC: profile and tweet content.
The first reference in Table 1involved a manual annotation task
in which raters were asked to label a Twitter account as human
or bot. The fourth study listed in the table is the same as the
first study; in this study, the same data set was evaluated by
both human annotators and machine learning methods [33].
There was a large discrepancy in predictive accuracy (F1-score)
between the two methods: 0.57 for the human annotators versus
0.92 for the automated method. Stated another way, human
participants correctly identified social bots less than 25% of the
time, though they were quite good at identifying genuine
(human) accounts (92%) and traditional spambots (91%). These
results suggest that social bots have a very different online
presence from traditional spambots, or “content polluters”—and
that this presence is convincingly human. Even if the human
annotators are compared to the lowest scoring automated method
(which we note is in a different domain and, thus, not directly
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comparable), the machine learning algorithm still provides a
considerable boost in F1-score (0.57 versus 0.72).
There is no good way to compare all automated methods
directly, as data sets are typically built in a single domain (ie,
a single social media platform) and rapid advances in machine
learning techniques prevent comparisons between models
published even a few years apart. Furthermore, results suggest
that models trained on highly curated bot data sets (eg, groups
of accounts promoting certain hashtags or spamming a particular
honeypot) may not perform well at detecting bots in other
contexts. Yang et al [52] used a large number of publicly
available bot data sets, training machine learning models on
each set and testing them on those remaining. The result was a
wide range of predictive accuracies across different bot data
sets.
How Do Bots Amplify and Spread
Misinformation?
We adopt the definition of misinformation used by Treen and
colleagues: “misleading [or false] information that is created
and spread, regardless of intent to deceive” [53]. For the
purposes of this paper, we include fake news and false
conspiracy theories under this umbrella term.
Many features of bots likely enable them to be “super-spreaders”
of misinformation. Bots have been shown to retweet articles
within seconds of their first being posted, contributing to the
articles going viral [15]. Moreover, the authors of this study
found that 33% of the top sharers of content from low-credibility
sources were likely to be bots, significantly higher than the
proportion of bots among top sharers of fact-checked content.
Similarly, in a study of bots and “anti-vaxxer” tweets, Yuan et
al [18] found that bots were “hyper-social,” disproportionately
contributing to content distribution. Bots also employ the
strategy of mentioning influential users, such as
@realDonaldTrump, in tweets linking to false or misleading
articles, and are more likely to do so than their human
counterparts [15]. The hope is that these users will share the
article with their many followers, contributing to its spread and
boosting its credibility. “Verified” (blue check) Twitter users,
often celebrities, have been shown to both author and propagate
COVID-19–related misinformation [54]. Interestingly, the
frequency of false claims about the 2020 election dropped
dramatically in the week after former president Donald Trump
was removed from the platform [55].
In light of findings that humans are largely unable to distinguish
social bots from genuine (human) accounts [33], it is likely that
humans unknowingly contribute to the spread of misinformation
as well. Accordingly, one study found that in regard to
low-credibility content, humans retweet bots and other humans
at the same rate [15]. Similarly, Vosoughi et al [56] found that
“fake news” articles spread faster on Twitter than true news
articles because humans, not bots, were more likely to retweet
fake articles. Given human susceptibility to both automated
accounts and “fake news,” some have warned that intelligent
social bots could be leveraged for mass deception or even
“political overthrow” [57].
There is reason to believe that bots have already infiltrated
political conversations online. Leading up to the 2016
presidential election in the United States, 20% of all political
tweets originated from accounts that were likely to be bots [16].
While it did not specifically implicate bots, one study found
that a majority of “fake” or extremely biased news articles
relating to the 2016 election were shared by unverified
accounts—that is, accounts that were not confirmed to be human
[58]. There is also evidence that bots spread misinformation in
the 2017 French presidential election, though ultimately the bot
campaign was unsuccessful, in part because the human users
who engaged with the bots were mostly foreigners with no say
in the election outcome [59]. Bot strategies specifically relevant
to political campaigns include “hashtag hijacking,” in which
bots adopt an opponent’s hashtags in order to spam or otherwise
undermine them, as well as flagging their opponent’s legitimate
content in the hopes it gets removed from the platform [60].
Where Do Bots Come From?
The origin of social bots is a challenging question to answer.
Given the aforementioned concerns of political disruption by
social bots, one may assume that foreign actors create social
bots to interfere with political processes. Indeed, the Mueller
report found evidence of Russian interference in the 2016 US
election via social media platforms [61], and Twitter reports
removing over 50,000 automated accounts with connections to
Russia [62]. However, locating the origin of a social media
account is difficult, as tweets from these accounts are rarely
geotagged. Rheault and Musulan [63] proposed a methodology
to identify clusters of foreign bots used during the 2019
Canadian election using uniform manifold approximation and
projection combined with user-level document embeddings.
Simply put, the authors constructed communities of users via
linguistic similarities, and identified members significantly
outside these communities as foreign bots.
Of note, studies have shown that a majority of social bots
focusing on election-related content originate domestically [63].
Reasons for a candidate or their supporters to employ social
bots may be relatively benign, such as boosting follower counts
or sharing news stories, or they may involve smear campaigns
[64].
While the ability to investigate the origin and motive of social
bots is difficult, the means to create a social bot are fairly easy
to access. Social bots are available for purchase on the dark
web, and there are tens of thousands of codes for building social
bots on free repositories like GitHub [65]. Of note, the top
contributors of bot-development tools for mainstream social
media sites are the United States, the United Kingdom, and
Japan. The authors of this paper also note the intelligence and
capabilities of these freely available bots may be overstated.
Are Bots Tweeting About COVID-19?
In light of the COVID-19 “infodemic” and findings that social
bots have contributed to misinformation spread in critical times,
we sought to assess the number of known Twitter bots producing
COVID-19–related content. To this end, we gathered a number
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of publicly available bot data sets from the Bot Repository [66].
These data sets include both traditional spambots and social
bots that were first identified through a number of different
methods (see the original papers for more details).
Using the open-source Python package TwitterMySQL [67],
which interfaces with the Twitter application programming
interface (API), we were able to pull all tweets from 2020 for
each bot in the combined data set. Of note, Twitter’s API limits
access to tweets and account information available at the time
of collection. Tweets and accounts that have been deleted or
made private since originally appearing in one of the above
papers are not made available, meaning we had less data than
what was reported in the original papers. Our final data set
consisted of 3.03 million tweets from 3953 bots, with an average
of 768.9 (SD 1145.4) tweets per bot, spanning January 1, 2020,
to August 21, 2020.
From these data, we pulled tweets using a set of 15
COVID-19–related keywords, which have previously been used
to identify COVID-19 tweets in a study tracking mental health
and psychiatric symptoms over time [68]. Sample keywords
include #coronavirus, #covid19, and #socialdistancing. We then
counted the number of accounts that mentioned these keywords
in tweets since January 2020. Table 2 shows the percentage of
bot accounts in each data set currently tweeting about
COVID-19. Original sample size refers to the number of bots
identified in this data set, while current sample size is the
number of currently active bots (ie, tweeting in 2020). Between
53% (96/182) and 66% (515/780) of these bots are actively
tweeting about COVID-19.
Table 2. Open-source data sets of bots discussing COVID-19a.
Bots discussing COVID-19, n (%)Current sample size, nOriginal sample size, nYearReference
1427 (54)262322,2232011Lee et al [36]
164 (56)2928262017Varol et al [41]
515 (66)78011302017Gilani et al [69]
48 (62)7749122017Cresci et al [33]
96 (53)1823912019Mazza et al [50]
aOriginal sample size is the number of bot IDs publicly released on the Bot Repository, while current sample size is the number of active accounts
tweeting in 2020. Percentage discussing COVID-19 is the percentage of bots with at least one tweet containing a COVID-19 keyword out of those active
in 2020.
Implications for the COVID-19 Pandemic
Here we have shown that a majority of known bots are tweeting
about COVID-19, a finding that corroborates similar studies
[68,70]. Early in the pandemic, one study found that 45% of
COVID-19–related tweets originate from bots [71], although
Twitter has pushed back on this claim, citing false-positive
detection algorithms [72]. Another study showed that COVID-19
misinformation on Twitter was more likely to come from
unverified accounts—that is, accounts not confirmed to be
human [7]. In an analysis of 43 million COVID-19–related
tweets, bots were found to be pushing a number of conspiracy
theories, such as QAnon, in addition to retweeting links from
partisan news sites [73]. Headlines from these links often
suggested that the virus was made in Wuhan laboratories or was
a biological weapon.
One limitation of our study is that we did not investigate the
validity of COVID-19–related claims endorsed by bots in our
analyses. It may be that bots are largely retweeting mainstream
news sources, as was the case in a recent study of bots using
#COVID19 or #COVID-19 hashtags [68]. However, previous
research has connected bots to the spread of misinformation in
other public health domains, such as vaccines [30] and
e-cigarettes [74], and unsubstantiated medical claims
surrounding the use of marijuana [75].
Such misinformation can have detrimental consequences for
the course of the COVID-19 pandemic. Examples of these
real-world consequences include shortages of
hydroxychloroquine (a drug that is crucial for treating lupus
and malaria) due to increased demand from people who believe
it will protect them from COVID-19 [76,77]. This drug has been
promoted as a preventative against COVID-19 on social media,
even though several randomized controlled trials have found it
ineffective, [78,79], and the National Institutes of Health
recently halted its own trial due to lack of effectiveness [80].
Moreover, belief in conspiracy theories about COVID-19 is
associated with a decreased likelihood of engaging in protective
measures such as frequent handwashing and social distancing,
suggesting that misinformation may even contribute to the
severity of the pandemic [81]. In addition, exposure to
misinformation has been negatively correlated with intention
to take a COVID-19 vaccine [82].
We are certainly not the first to express concern with viral
misinformation; in May 2020, Twitter began labeling fake or
misleading news related to COVID-19 in an effort to ensure the
integrity of information shared on the platform [83]. Facebook
introduced even more controls, such as organizing the most
vetted articles at the top of the news feed, banning antimask
groups, and sending antimisinformation messages to users who
have shared fake news [84]. However, these measures are
designed to target humans. In light of the numerous viral rumors
relating to COVID-19 and the US response to the pandemic,
we believe that bots likely contributed to their spread.
Major social media platforms like Twitter and Facebook do
have methods to curtail suspected bots. In 2018, Twitter banned
close to 70 million suspicious accounts in a matter of months
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[85]. Facebook banned 1.3 billion suspicious accounts in the
third quarter of 2020. The platform estimates these accounts
represent 5% of its worldwide monthly active users. The vast
majority of suspicious accounts were identified using automated
detection methods, but 0.7% were first flagged by human users,
suggesting that everyday Facebook users concerned about
malicious activity on the platform can contribute to efforts to
ban these accounts [86].
Mitigation of the harmful effects of social bots can also occur
at the policy level. In 2018, California became the first and only
state to pass a law requiring social bots to identify themselves
as such [87]. In 2019, Senator Dianne Feinstein proposed a
similar bill federally; the bill would allow the Federal Trade
Commission to enforce bot transparency and would prohibit
political candidates from incorporating social bots in their
campaign strategy [88]. The United States Congress has brought
top executives from Facebook, Twitter, and Google to testify
before Congress about Russian influence on their platforms in
advance of the 2016 election [89]. Scholars have interpreted
these actions as a sign that the government wishes to maintain
the right to regulate content on social media—a prospect that
brings concerns of its own [90]. Presently, content problems on
social media platforms are almost exclusively dealt with by the
owners of those platforms, usually in response to user
complaints, but in the coming years we may see an increase in
government oversight on these platforms, fueling concerns about
state-sponsored censorship [91,92]. More fundamentally, some
have argued that, before any actionable policy or automatic
interventions can be enabled, ambiguities in both bot definitions
and jurisdiction and authority need to be addressed [90].
Even as citizens, social media platforms, and policy makers
converge on the notion that bots and misinformation are urgent
problems, the methods used to address the issue have had mixed
results. When social media platforms crack down on bots and
misinformation, either through automated techniques or manual
content moderation, they run the risk of censoring online speech
and further disenfranchising minority populations. Content
promotion and moderation can lead to arbitrary policy decisions
that may be inconsistent across or even within platforms [93].
In one example, Facebook ignited a controversy when their
moderators flagged a breastfeeding photo as obscene, leading
to a large number of protests on both sides of the debate [94].
Automated methods suffer from similar drawbacks, with
multiple studies showing that biases in machine learning models
can have unintended downstream consequences [95]. For
example, algorithms designed to detect hate speech were more
likely to label a post as “toxic” when it showed markers of
African American English [96].
Finally, there is a continued arms race between bot-detection
algorithms and bot creators [21,33]. As bots inevitably become
more intelligent and convincingly human, the means for
identifying them will have to become more precise. We observed
that the majority of known bots in a sample of publicly available
data sets are now tweeting about COVID-19. These bots,
identified between 2011 and 2019, were discovered before the
pandemic and were originally designed for non–COVID-19
purposes: promoting product hashtags, retweeting political
candidates, and spreading links to malicious content. The
COVID-19 pandemic will eventually end, but we have reason
to believe social bots, perhaps even the same accounts, will
latch on to future global issues. Additionally, we can expect bot
generation techniques to advance, especially as deep learning
methods continue to improve on tasks such as text or image
generation [97,98]. Bot creators will continue to deploy such
techniques, possibly fooling detection algorithms and humans
alike. In the end, we should not expect current detection
techniques, self-policing of social media platforms, or public
officials alone to fully recognize, or adequately address, the
current landscape of bots and misinformation.
Acknowledgments
This work was supported in part by the Intramural Research Program of the National Institutes of Health, National Institute on
Drug Abuse. MHW wrote the manuscript, with help from SG and AD. SG conceptualized the paper, with input from BC, HAS,
and LU. SG performed the analyses on Twitter bots and created Tables 1 and 2. MHW, SG, AD, and MR all contributed to the
literature review. LL and DHE provided crucial edits. All authors reviewed and approved the final version of the manuscript.
Conflicts of Interest
None declared.
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Abbreviations
API: application programming interface
Edited by R Kukafka, C Basch; submitted 04.01.21; peer-reviewed by A Agarwal, D Yeung, N Martinez-Martin; comments to author
16.02.21; revised version received 04.03.21; accepted 16.04.21; published 20.05.21
Please cite as:
Himelein-Wachowiak M, Giorgi S, Devoto A, Rahman M, Ungar L, Schwartz HA, Epstein DH, Leggio L, Curtis B
Bots and Misinformation Spread on Social Media: Implications for COVID-19
J Med Internet Res 2021;23(5):e26933
URL: https://www.jmir.org/2021/5/e26933
doi: 10.2196/26933
PMID: 33882014
©McKenzie Himelein-Wachowiak, Salvatore Giorgi, Amanda Devoto, Muhammad Rahman, Lyle Ungar, H Andrew Schwartz,
David H Epstein, Lorenzo Leggio, Brenda Curtis. Originally published in the Journal of Medical Internet Research
(https://www.jmir.org), 20.05.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution
License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete
bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license
information must be included.
J Med Internet Res 2021 | vol. 23 | iss. 5 | e26933 | p. 11https://www.jmir.org/2021/5/e26933 (page number not for citation purposes)
Himelein-Wachowiak et alJOURNAL OF MEDICAL INTERNET RESEARCH
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