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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.
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 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 BotsOverview
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].
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 1 provides 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.
Review of state-of-the-art detection of bots on Facebook and Twitter.
Type and reference
Platform
Number of accounts
Features
Model
Metric
Predictive accuracy
Na
Tb
Cc
Human judgment (manual annotation)
Cresci et al (2017) [33]
Twitter
928
Manual annotation
F1-score
0.57
Automatic methods
Ahmed and Abulaish (2013) [27]
Facebook and Twitter
320 (Facebook), 305 (Twitter)
✓
Naïve Bayes, decision trees, rule learners
Detection rate
0.96 (Facebook), 0.99 (Twitter)
Dickerson et al (2014) [42]
Twitter
897
✓
✓
✓
Gradient boosting
Area under the curve
0.73
Cresci et al (2017) [33]
Twitter
928
✓
Digital DNA sequences
F1-score
0.92
Varol et al (2017) [41]
Twitter
21,000
✓
✓
✓
Random forests
Area under the curve
0.95
Kudugunta and Ferrara (2018) [49]
Twitter
8386
✓
AdaBoost
Area under the curve
>0.99
Mazza et al (2019) [50]
Twitter
1000
✓
Long short-term memory networks
F1-score
0.87
Santia et al (2019) [51]
Facebook
1000
✓
Support vector machines, decision trees, Naïve Bayes
F1-score
0.72
Yang et al (2020) [52]
Twitter
137,520
✓
✓
Random forests
Area under the curve
0.60-0.99
aN: network properties.
bT: account activity and temporal patterns.
cC: profile and tweet content.
The first reference in Table 1 involved 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 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 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.
Open-source data sets of bots discussing COVID-19a.
Reference
Year
Original sample size, n
Current sample size, n
Bots discussing COVID-19, n (%)
Lee et al [36]
2011
22,223
2623
1427 (54)
Varol et al [41]
2017
826
292
164 (56)
Gilani et al [69]
2017
1130
780
515 (66)
Cresci et al [33]
2017
4912
77
48 (62)
Mazza et al [50]
2019
391
182
96 (53)
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 [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.
AbbreviationsAPI
application programming interface
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.
None declared.
Coronavirus Resource Center2020-12-21https://coronavirus.jhu.edu/map.htmlGozziNTizzaniMStarniniMCiullaFPaolottiDPanissonAPerraNCollective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis202010122210e2159710.2196/2159732960775v22i10e21597PMC7553788NielsenRKFletcherRNewmanNBrennenJSHowardPN2020042021-05-09Reuters Institutehttp://www.fundacionindex.com/fi/wp-content/uploads/2020/04/Navigating-the-Coronavirus-Infodemic-FINAL.pdfValikaTSMaurrasseSEReichertLA Second Pandemic? Perspective on Information Overload in the COVID-19 Era202011163593193310.1177/019459982093585032513072BallPMaxmenAThe Epic Battle Against Coronavirus Misinformation and Conspiracy Theories202005581780937137410.1038/d41586-020-01452-z3246165810.1038/d41586-020-01452-zTangcharoensathienVCallejaNNguyenTPurnatTD'AgostinoMGarcia SaisoSLandryMRashidianAHamiltonCAbdAllahAGhigaIHillAHougendoblerDVan AndelJNunnMBrooksISaccoPLDe DomenicoMMaiPGruzdAAlaphilippeABriandSFramework for Managing the COVID-19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation20200626226e1965910.2196/1965932558655KouzyRAbi JaoudeJKraitemAEl AlamMBKaramBAdibEZarkaJTraboulsiCAklEWBaddourKCoronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter20200313123e725510.7759/cureus.725532292669PMC7152572ShahiGDirksonAMajchrzakTAn exploratory study of COVID-19 misinformation on Twitter2021032210010410.1016/j.osnem.2020.10010433623836S2468-6964(20)30045-8PMC7893249OhOKwonKHRaoHRAn exploration of social media in extreme events: Rumor theory and Twitter during the Haiti earthquake 20102010International Conference on Information Systems 20202010St. Louis, MOWilliamsonEHow Alex Jones and Infowars helped a Florida man torment Sandy Hook families201903292021-05-09https://www.nytimes.com/2019/03/29/us/politics/alex-jones-infowars-sandy-hook.htmlWangBZhuangJRumor response, debunking response, and decision makings of misinformed Twitter users during disasters20185119331145116210.1007/s11069-018-3344-6GuptaALambaHKumaraguruP$1.00 per RT #BostonMarathon #PrayForBoston: Analyzing fake content on Twitter20132013 APWG eCrime Researchers SummitSeptember 17-18, 2013San Francisco, CA11210.1109/ecrs.2013.6805772StarbirdKMaddockJOrandMAchtermanPMasonRMRumors, False Flags, and Digital Vigilantes: Misinformation on Twitter after the 2013 Boston Marathon Bombing2014iConference 2014March 4-7, 2014Berlin, Germany65466210.9776/14308JinFWangWZhaoLDoughertyECaoYLuCRamakrishnanNMisinformation Propagation in the Age of Twitter2014124712909410.1109/MC.2014.361ShaoCCiampagliaGLVarolOYangKFlamminiAMenczerFThe spread of low-credibility content by social bots2018112091478710.1038/s41467-018-06930-73045941510.1038/s41467-018-06930-7PMC6246561BessiAFerraraESocial bots distort the 2016 U.S. Presidential election online discussion201611032111110.5210/fm.v21i11.7090BadawyAFerraraELermanKAnalyzing the digital traces of political manipulation: The 2016 Russian interference Twitter campaign2018IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)August 28-31, 2018Barcelona, Spain25826510.1109/asonam.2018.8508646YuanXSchuchardRJCrooksATExamining Emergent Communities and Social Bots Within the Polarized Online Vaccination Debate in Twitter201909045320563051198654610.1177/2056305119865465LeggioLGarbuttJCAddoloratoGEffectiveness and safety of baclofen in the treatment of alcohol dependent patients20100391334410.2174/18715271079096661420201813BSP/CDTCNSND/E-Pub/00010ZhuYGuntukuSCLinWGhineaGRediJAMeasuring Individual Video QoE: A survey, and proposal for future directions using social media20180522142s12410.1145/3183512YangKCVarolODavisCAFerraraEFlamminiAMenczerFArming the public with artificial intelligence to counter social bots2019020611486110.1002/hbe2.115SavageSMonroy-HernandezAHollererTBotivist: Calling volunteers to action using online bots201616th ACM Conference on Computer-Supported Cooperative Work & Social ComputingFebruary 27-March 2, 2016San Francisco, CA81382210.1145/2818048.2819985AbokhodairNYooDMcDonaldDWDissecting a Social Botnet: Growth, Content and Influence in Twitter201518th ACM Conference on Computer-Supported Cooperative Work & Social ComputingMarch 14-18, 2015Vancouver, Canada83985110.1145/2675133.2675208BastosMTMerceaDThe Brexit Botnet and User-Generated Hyperpartisan News20171010371385410.1177/0894439317734157JhaverSBruckmanAGilbertEDoes Transparency in Moderation Really Matter?: User Behavior After Content Removal Explanations on Reddit20193CSCW12710.1145/3359252MaMCLalorJPAn Empirical Analysis of Human-Bot Interaction on Reddit2020Sixth Workshop on Noisy User-generated Text (W-NUT 2020)November 2020VirtualAssociation for Computational Linguistics10110610.18653/v1/2020.wnut-1.14AhmedFAbulaishMA generic statistical approach for spam detection in Online Social Networks201363610-111120112910.1016/j.comcom.2013.04.004HussainMNTokdemirSAgarwalNAl-KhateebSAnalyzing disinformation and crowd manipulation tactics on YouTube2018IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)August 28-31, 2018Barcelona, SpainIEEE1092109510.1109/asonam.2018.8508766SubrahmanianVAzariaADurstSKaganVGalstyanALermanKZhuLFerraraEFlamminiAMenczerFThe DARPA Twitter Bot Challenge20166496384610.1109/MC.2016.18327295638BroniatowskiDAJamisonAMQiSAlKulaibLChenTBentonAQuinnSCDredzeMWeaponized Health Communication: Twitter Bots and Russian Trolls Amplify the Vaccine Debate201810108101378138410.2105/AJPH.2018.30456730138075PMC6137759FerraraEVarolODavisCMenczerFFlamminiAThe rise of social bots201606245979610410.1145/2818717ZhangJZhangRZhangYYanGThe Rise of Social Botnets: Attacks and Countermeasures20181111561068108210.1109/tdsc.2016.2641441CresciSDi PietroRPetrocchiMSpognardiATesconiMThe paradigm-shift of social spambots: Evidence, theories, and tools for the arms race2017The 26th International Conference on World Wide Web CompanionApril 3-7, 2017Perth, Australia96397210.1145/3041021.3055135ChuZGianvecchioSWangHJajodiaSDetecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?2012119681182410.1109/Tdsc.2012.75ClarkEMWilliamsJRJonesCAGalbraithRADanforthCMDoddsPSSifting robotic from organic text: A natural language approach for detecting automation on Twitter201609161710.1016/j.jocs.2015.11.002LeeKEoffBCaverleeJSeven months with the devils: A long-term study of content polluters on Twitter2011Fifth International AAAI Conference on Weblogs and Social Media (ICWSM)July 17-21, 2011Barcelona, SpainSpitznerLThe Honeynet Project: Trapping the hackers2003312152310.1109/msecp.2003.1193207PrinceMBDahlBMHollowayLKellerAMLangheinrichEUnderstanding how spammers steal your email address: An analysis of the first six months of data from Project Honey Pot2005Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS)July 21-22, 2005Stanford, CAWebbSCaverleeJPuCSocial honeypots: Making friends with a spammer near you2008Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS)August 21-22, 2008Mountain View, CA110DavisCAVarolOFerraraEFlamminiAMenczerFBotornot: A system to evaluate social bots201625th International Conference Companion on World Wide WebApril 11-15, 2016Montreal, Canada10.1145/2872518.2889302VarolOFerraraEDavisCAMenczerFFlamminiAOnline human-bot interactions: Detection, estimation, and characterization2017International AAAI Conference on Web and Social MediaMay 15-18, 2017Montreal, CanadaDickersonJPKaganVSubrahmanianVUsing sentiment to detect bots on twitter: Are humans more opinionated than bots?2014IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)Aug 17-20, 2014Beijing, China10.1109/asonam.2014.6921650StephensMPoorthuisAFollow thy neighbor: Connecting the social and the spatial networks on Twitter20150953879510.1016/j.compenvurbsys.2014.07.002YangZWilsonCWangXGaoTZhaoBYDaiYUncovering social network Sybils in the wild2014028112910.1145/2556609PozzanaIFerraraEMeasuring Bot and Human Behavioral Dynamics2020422812510.3389/fphy.2020.00125SahamiMDumaisSHeckermanDHorvitzEA Bayesian approach to filtering junk email19982021-05-11https://www.aaai.org/Papers/Workshops/1998/WS-98-05/WS98-05-009.pdfJagaticTNJohnsonNAJakobssonMMenczerFSocial phishing20071050109410010.1145/1290958.1290968StringhiniGKruegelCVignaGDetecting spammers on social networks2010Annual Computer Security Applications ConferenceDecember 6-10, 2010Austin, TX1910.1145/1920261.1920263KuduguntaSFerraraEDeep neural networks for bot detection20181046731232210.1016/j.ins.2018.08.019MazzaMCresciSAvvenutiMQuattrociocchiWTesconiMRTbust: Exploiting temporal patterns for botnet detection on Twitter2019ACM Conference on Web ScienceJune 30, 2019Boston, MA, USA10.1145/3292522.3326015SantiaGCMujibMIWilliamsJRDetecting Social Bots on Facebook in an Information Veracity Context2019International AAAI Conference on Web and Social MediaJune 11, 2019Munich, GermanyYangKCVarolOHuiPMMenczerFScalable and Generalizable Social Bot Detection through Data Selection2020AAAI Conference on Artificial IntelligenceFebruary 7-12, 2020New York, NY1096110310.1609/aaai.v34i01.5460TreenKMWilliamsHTO'NeillSJOnline misinformation about climate change20200618115e66510.1002/wcc.665ShahiGKDirksonAMajchrzakTAAn exploratory study of COVID-19 misinformation on Twitter2021032210010410.1016/j.osnem.2020.10010433623836S2468-6964(20)30045-8PMC7893249DwoskinETimbergCMisinformation dropped dramatically the week after Twitter banned Trump and some allies202101162021-05-09https://www.washingtonpost.com/technology/2021/01/16/misinformation-trump-twitter/VosoughiSRoyDAralSThe spread of true and false news online2018030935963801146115110.1126/science.aap955929590045359/6380/1146WangPAngaritaRRennaIIs this the era of misinformation yet: combining social bots and fake news to deceive the masses2018Companion Proceedings of The Web Conference 2018April 23-27, 2018Lyon, France1557156110.1145/3184558.3191610BovetAMakseHAInfluence of fake news in Twitter during the 2016 US presidential election201912101110.1038/s41467-018-07761-2FerraraEDisinformation and social bot operations in the run up to the 2017 French presidential election20170731228110.5210/fm.v22i8.8005HowardPNHow Political Campaigns Weaponize Social Media Bots20182021-05-11https://spectrum.ieee.org/computing/software/how-political-campaigns-weaponize-social-media-botsMuellerRSReport on the investigation into Russian interference in the 2016 presidential election2021-05-09https://www.justice.gov/storage/report.pdfTwitterUpdate on Twitter's review of the 2016 US election201801192021-05-09https://blog.twitter.com/en_us/topics/company/2018/2016-election-update.htmlRheaultLMusulanAEfficient detection of online communities and social bot activity during electoral campaigns2021020211410.1080/19331681.2021.1879705HowardPNWoolleySCaloRAlgorithms, bots, and political communication in the US 2016 election: The challenge of automated political communication for election law and administration20180411152819310.1080/19331681.2018.1448735AssenmacherDCleverLFrischlichLQuandtTTrautmannHGrimmeCDemystifying Social Bots: On the Intelligence of Automated Social Media Actors202009016320563051209392610.1177/2056305120939264VarolO2020-11-20https://botometer.osome.iu.edu/bot-repository/index.htmlGiorgiSSapM2020-11-20https://github.com/dlatk/TwitterMySQLAl-RawiAShuklaVBots as Active News Promoters: A Digital Analysis of COVID-19 Tweets20200927111046110.3390/info11100461GilaniZFarahbakhshRTysonGWangLCrowcroftJOf bots and humans (on Twitter)2017IEEE/ACM International Conference on Advances in Social Networks Analysis and MiningJuly 31, 2017Sydney, Australia10.1145/3110025.3110090ShiWLiuDYangJZhangJWenSSuJSocial Bots' Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter202011231722870110.3390/ijerph1722870133238567ijerph17228701PMC7709024AllynBResearchers: Nearly half of accounts tweeting about coronavirus are likely bots202005202021-05-09https://www.npr.org/sections/coronavirus-live-updates/2020/05/20/859814085/researchers-nearly-half-of-accounts-tweeting-about-coronavirus-are-likely-botsRothYPicklesNBot or not? The facts about platform manipulation on Twitter202005182021-05-09https://blog.twitter.com/en_us/topics/company/2020/bot-or-not.htmlFerraraEWhat types of COVID-19 conspiracies are populated by Twitter bots?20200519256110.5210/fm.v25i6.10633AllemJFerraraEUppuSPCruzTBUngerJBE-Cigarette Surveillance With Social Media Data: Social Bots, Emerging Topics, and Trends2017122034e9810.2196/publichealth.864129263018v3i4e98PMC5752967AllemJEscobedoPDharmapuriLCannabis Surveillance With Twitter Data: Emerging Topics and Social Bots202003110335736210.2105/AJPH.2019.30546131855475PMC7002948MehtaBSalmonJIbrahimSPotential Shortages of Hydroxychloroquine for Patients with Lupus During the Coronavirus Disease 2019 Pandemic2020041014e20043810.1001/jamahealthforum.2020.0438Lupus Research Alliance202005282021-05-09https://www.lupusresearch.org/covid-19-caused-hydroxychloroquine-issues-for-third-of-lupus-patients-new-lra-survey-finds/SkipperCPPastickKAEngenNWBangdiwalaASAbassiMLofgrenSMWilliamsDAOkaforECPullenMFNicolMRNasceneAAHullsiekKHChengMPLukeDLotherSAMacKenzieLJDrobotGKellyLESchwartzISZarychanskiRMcDonaldEGLeeTCRajasinghamRBoulwareDRHydroxychloroquine in Nonhospitalized Adults With Early COVID-19: A Randomized Trial20201020173862363110.7326/M20-420732673060PMC7384270MitjàOCorbacho-MonnéMUbalsMTebeCPeñafielJTobiasABallanaEAlemanyARiera-MartíNPérezCASuñerCLaportePAdmellaPMitjàJCluaMBertranLSarquellaMGavilánSAraJArgimonJMCasabonaJCuatrecasasGCañadasPElizalde-TorrentAFabregatRFarréMForcadaAFlores-MateoGMuntadaENadalNNarejosSGil-OrtegaANPratNPuigJQuiñonesCReyes-UreñaJRamírez-ViaplanaFRuizLRiveira-MuñozESierraAVelascoCVivanco-HidalgoRMSentísAG-BeirasCClotetBVall-MayansMBCN PEP-CoV-2 RESEARCH GROUPHydroxychloroquine for Early Treatment of Adults with Mild Covid-19: A Randomized-Controlled Trial20200716110.1093/cid/ciaa1009326741265872589PMC7454406National Institutes of HealthNIH halts clinical trial of hydroxychloroquine202006202021-05-09https://www.nih.gov/news-events/news-releases/nih-halts-clinical-trial-hydroxychloroquineAllingtonDDuffyBWesselySDhavanNRubinJHealth-protective behaviour, social media usage and conspiracy belief during the COVID-19 public health emergency202006091710.1017/S003329172000224X32513320S003329172000224XPMC7298098LoombaSde FigueiredoAPiatekSJde GraafKLarsonHJMeasuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA202103055333734810.1038/s41562-021-01056-13354745310.1038/s41562-021-01056-1RothYPicklesNUpdating our approach to misleading information202005112021-05-09https://blog.twitter.com/en_us/topics/product/2020/updating-our-approach-to-misleading-information.htmlStattNFacebook will now show a warning before you share articles about COVID-19202008122021-05-09https://www.theverge.com/2020/8/12/21365305/facebook-covid-19-warning-notification-post-misinformationTimbergCDwoskinETwitter is sweeping out fake accounts like never before, putting user growth at risk201807092021-05-09https://www.washingtonpost.com/technology/2018/07/06/twitter-is-sweeping-out-fake-accounts-like-never-before-putting-user-growth-risk/FacebookCommunity Standards Enforcement Report2021-05-09https://transparency.facebook.com/community-standards-enforcementKamalGCalifornia's BOT Disclosure Law, SB 1001, now in effect201907152021-05-09https://www.natlawreview.com/article/california-s-bot-disclosure-law-sb-1001-now-effectFrazinRFeinstein introduces bill to prohibit campaigns from using social media bots201907162021-05-09https://thehill.com/policy/cybersecurity/453336-dem-senator-introduces-bill-to-prohibit-campaigns-from-using-botsKangCFandosNIssacMTech executives are contrite about election meddling, but make few promises on Capitol Hill201710312021-05-09https://www.nytimes.com/2017/10/31/us/politics/facebook-twitter-google-hearings-congress.htmlGorwaRGuilbeaultDUnpacking the Social Media Bot: A Typology to Guide Research and Policy2018081012222524810.1002/poi3.184SamplesJWhy the government should not regulate content moderation of social media201904092021-05-09https://www.cato.org/publications/policy-analysis/why-government-should-not-regulate-content-moderation-social-mediaCrews JrCWThe Case against Social Media Content Regulation: Reaffirming Congress’ Duty to Protect Online Bias, 'Harmful Content,' and Dissident Speech from the Administrative State20200628Competitive Enterprise Institute, Issue Analysis134GillespieT2018New Haven, CTYale University PressIbrahimYThe breastfeeding controversy and Facebook: Politicization of image, privacy and protest201012162810.4018/jep.2010040102DixonLLiJSorensenJThainNVassermanLMeasuring and mitigating unintended bias in text classification2018AAAI/ACM Conference on AI, Ethics, and Society2018New Orleans, LA677310.1145/3278721.3278729SapMCardDGabrielSChoiYSmithNThe risk of racial bias in hate speech detection2019Annual Meeting of the Association for Computational LinguisticsJuly 2019Florence, Italy1668167810.18653/v1/p19-1163ZhaoJXiongLJayashreePDual-agent GANs for photorealistic and identity preserving profile face synthesis2017Advances in Neural Information Processing SystemsDecember 4, 2017Long Beach, CA, USA6676BrownTBMannBRyderNSubbiahMKaplanJDhariwalPLanguage models are few-shot learners2020Advances in Neural Information Processing SystemsDecember 6, 2020Virtual18771901