Acting the Part: Examining Information Operations Within #BlackLivesMatter Discourse


Proceedings of the ACM on Human-Computer Interaction, Vol. 2, No. CSCW, Article 20, Publication date: November 2018. 

Acting the Part: Examining Information Operations Within 
#BlackLivesMatter Discourse 

AHMER ARIF, Human Centered Design & Engineering, University of Washington, USA 
LEO G. STEWART, Information School, University of Washington, USA 
KATE STARBIRD, Human Centered Design & Engineering, University of Washington, USA 
 

Information campaigns that seek to tap into and manipulate online discussions are becoming an issue of 
increasing public concern. Social media companies are now problematizing some campaigns, specifically 
those that intentionally obscure their origins, as ‘information operations’. This research examines how 
social media accounts linked to one such operation—allegedly conducted by Russia’s Internet Research 
Agency—participated in an online discourse about the #BlackLivesMatter movement and police-related 
shootings in the U.S. during 2016. We study the interactions of these accounts within the online crowd 
using interpretative analysis of a network graph based on retweet flows in combination with a qualitative 
content analysis. Our empirical findings show how these accounts imitated ordinary users to systematically 
micro-target different audiences, foster antagonism and undermine trust in information intermediaries. 
Conceptually, this research enhances our understanding of how information operations can leverage the 
interactive social media environment to both reflect and shape existing social divisions.  

CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social 
computing • Human-centered computing → Social media  

KEYWORDS 
Social media; Twitter; Information Operations; Disinformation; Media Manipulation; Black Lives Matter 

ACM Reference format: 
Ahmer Arif, Leo G. Stewart, and Kate Starbird. 2018. Acting the Part: Examining Information Operations Within 
#BlackLivesMatter Discourse. In Proceedings of the ACM on Human-Computer Interaction, Vol. 2, CSCW, Article 20 
(November 2018). ACM, New York, NY. 27 pages. https://doi.org/10.1145/3274289 

1 INTRODUCTION 
Although the advent of social media was initially met with enthusiasm for more democratic 
information systems, our evolving information practices are now forcing us to think about how 
these new points of access can be manipulated. This has become a more urgent consideration in 
recent years as social media platforms have allowed misinformation—as well as disinformation, 
and political propaganda—to spread and engage audiences in new ways. Recently, social media 
companies have acknowledged that their platforms have become sites for information operations, 
i.e. actions taken by governments or organized non-state actors to manipulate public opinion [59, 
60, 66]. Though information operations are not new, their intersection with social media is not 
well understood. 

This study focuses on inauthentic social media accounts as a component of information 
operations to consider how they harness the sociotechnical infrastructure of social media 
platforms for their benefit. The accounts that we analyze were publicly suspended by Twitter for  

 
This research is a collaboration between the emCOMP lab and DataLab at the University of Washington and was supported by National 
Science Foundation grant 1749815 and Office of Naval Research grants N000141712980 and N000141812012.  

Author’s addresses: ahmer@uw.edu, lgs17@uw.edu, kstarbi@uw.edu 

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mailto:ahmer@uw.edu
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https://doi.org/10.1145/3274289


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being affiliated with the Internet Research Agency (RU-IRA), a Russian organization based in St. 
Petersburg that has been formally indicted by the U.S. government for engaging in professional 
propaganda, including hiring 80 full-time employees to use social media accounts while 
pretending to be U.S. citizens [61]. Despite mounting allegations, the tactics used by the social 
media accounts linked to these efforts have not yet been systematically examined. 

We investigate how these RU-IRA affiliated accounts participated in an online discourse about 
the #BlackLivesMatter movement and shootings in the U.S. during 2016. We did not select this 
discourse or collect our initial data with the intent to study information operations. Instead, we 
had previously scoped and analyzed this data in work examining “framing contests” within 
politically charged discourse on Twitter [55]. Later, when Twitter released a list of RU-IRA 
affiliated accounts during formal hearings with the U.S. House of Representatives Select 
Committee on Intelligence [62], we recognized several accounts from our earlier work. This led 
us to ask ourselves: were more of these accounts present in the data we collected, and if so, with 
whom did they interact, and what were they doing? 

We approach these questions from a CSCW perspective, adapting methods from the field of 
crisis informatics [2, 34, 42] to analyze both the large-scale interactions between these accounts 
and other members of these online communities, and the specific online actions that the 
operators of these accounts took as they worked to infiltrate and influence these communities. To 
answer the first of our questions—if Russian information operations were active in the 
#BlackLivesMatter discourse—we used a network graph of retweets to learn that at least 29 of 
these accounts did have a meaningful presence within the information flows of this discourse. 
The graph also revealed that different RU-IRA accounts were participating on both “sides” of the 
conversation—within two structurally distinct communities. Then, to understand what these RU-
IRA accounts were doing, we launched a multi-sited qualitative investigation into the messages, 
personas, and interactions of these accounts. As we immersed ourselves in their content, our 
questions about what these accounts were doing evolved. We asked: Who did these accounts 
attempt to mimic? What did these accounts do to produce and maintain their personas? What 
were these personas used to model and project in the discourse that we studied? To what extent 
did these ‘performances’ seem to adhere to a common script or set of constraints and where did 
they deviate from each other? 

Addressing these questions contributes to a fuller account of the dynamics that emerge 
between information operations and those who use social media platforms for cooperative work 
such as grassroots political organizing [49], disaster response [12, 30, 67], and more broadly the 
collective activity to consume and elevate breaking news [64]. Our findings suggest that 
information operations were occurring in this context and that while social media platforms may 
intend to bring us together, at least some of these platforms are being targeted, deliberately, to 
pull us apart. On another level, this research helps us see that the ‘work’ these accounts were 
doing to facilitate information operations goes beyond publishing biased information. The work 
can also be seen as an improvised performance being carried out by an account operator (or, 
perhaps, a small team of operators) to try and ‘inspire’ the online communities they target. These 
performances can involve connecting to cultural narratives that people know, enacting 
stereotypes, and modeling how to react to information. This has implications for platform 
designers as they consider the strategies they will use—or more specifically, the policies they will 
create to guide the strategies they will use—to address information operations.  

 



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2 LITERATURE REVIEW 

In this literature review, we first provide background on information operations generally and on 
their emerging use in the online sphere. Within that accounting, we highlight a specific 
(theorized) goal of information operations related to the concept of disinformation that is relevant 
to the study presented here, and explain how our research contibutes to better understanding 
that goal and the tactics used to achieve it. Finally, we explain how approaching this topic from a 
CSCW lens helps to conceptualize the activities of these accounts as a type of online “work” 
conducted by an information operator (or agent) in interaction with an online crowd. 

2.1 Information Operations 

Information operations is a term employed by the U.S. intelligence community to describe actions 
taken to disrupt the information streams and information systems of a geopolitical adversary 
[28]. These actions focus on degrading the decision-making capabilities of others through non 
rational means (e.g. deception and psychological warfare) [3, 29]. Unlike ‘information warfare’ 
which is generally conducted during actual combat, information operations can be carried out in 
peacetime environments to influence civil affairs [3]. Consequently, these operations are 
increasingly considered a ‘soft’ yet formidable alternative to ‘hard power’ or ‘hard warfare’, 
targeting perception and cognition rather than launching physical attacks on infrastructure [10, 
32, 45].  

Some academics [16, 32, 40] and journalists [45] have theorized that a primary or secondary 
goal of many information operations is not necessarily to convince someone of something, but to 
strategically direct discourse in ways that “kill the possibility of debate and a reality-based 
politics” [45]. By eliciting confusion, division, disenchantment, and paranoia, information 
operations can potentially serve to silence political dissent, enable historical revisionism, and 
hinder collaboration [16, 32, 68]. Both journalists and former intelligence professionals have 
suggested that such efforts can be tied to historical strategies of dezinformatsiya [5, 45, 52], a 
Russian term that translates to disinformation and describes the intentional spread of false or 
inaccurate information meant to mislead others about the state of the world.  

Disinformation can therefore be viewed as a specific form of information operation that has its 
historical roots in tactics initially developed and deployed by the Soviet Union [45, 52]. These 
tactics have been characterized as having an ‘ideological fluidity’ allowing them to overlap with a 
range of oppositional political groups—with the goal of fostering social division [43]. The core of 
these tactics involves harnessing existing public discontent by amplifying reductive social 
interpretations that confirm existing beliefs, support desired conclusions, or prompt certain 
strong emotions regarding groups of people and events [16, 32]. By strategically and 
opportunistically tapping into latent social fractures—as in cases surrounding the Ku Klux Klan 
as well as the AIDS and Ebola epidemics—trust in civil institutions and information 
intermediaries can be undermined [5, 32, 45].  

The clandestine nature of information operations means that our current understanding of the 
relationship between existing social rifts and disinformation tactics remains speculative. Our 
work empirically examines this relationship by systematically exploring what RU-IRA affiliated 
accounts were doing in a discourse that is already deeply segregated in terms of politics and race. 

2.2 Information Operations on Social Media 

The announcements by Facebook, Twitter and Tumblr [59, 60, 66] reveal that social networking 
sites have become a front for information operations—a front that can be accessed from nearly 
anywhere in the world, by nearly anyone, and where users may be particularly vulnerable. 



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Researchers have noted that the interactivity afforded by these social computing systems can 
allow information operations to produce emergent and self-reinforcing effects [10, 46]. Moreover, 
this new media ecosystem is dominated by increasingly partisan news sources [20], political 
homophily [22, 31], and algorithmically derived newsfeeds being skimmed by audiences that are 
trying to cope with the cascades of information before them. These structural issues can 
contribute to the effectiveness of information operations, including disinformation. At the same 
time, increasing protection against information manipulation on these platforms risks 
undermining the free speech and open discourse foundational to democracies [32, 68]. 

2.3 Information Operations as Collaborative Work 

Researchers have noted that the ’work’ of information operations on social media is, in principle, 
collaborative in the sense that high-level digital marketing strategists and political clients work 
together to design campaign objectives which are then implemented and shaped by a multitude 
of different actors [40]. Tucker et al. [58] partially capture the complexity of this assemblage by 
noting how bots, fake-news websites, conspiracy theorists, trolls, highly partisan media outlets, 
the mainstream media, influential bloggers, and ordinary citizens are now all playing 
overlapping—and even competing—roles in producing and amplifying propaganda in the social 
media ecosystem. Relevant here, these authors note that hired trolls or anonymous influencers 
that use fake online profiles to support disinformation campaigns are a relatively understudied 
set of actors partially due to the difficulties involved in identifying them [58]. Our research helps 
to address this gap. 

Although impersonating others to spread harmful narratives is an old practice (e.g. the forged 
1903 pamphlet, Protocols of the Learned Elders of Zion that was used to justify anti-Semitic 
agendas) [29], its intersection with the networked media environment is not well understood. 
What we do know is that impersonation is now being used to amplify racist narratives [17, 18] 
and mobilize digital workers being paid to act like grassroot activists in a variety of work 
arrangements. For instance, Rongbin Han’s research [26] on the digital political operations of 
China’s “fifty-cent army” surfaces efforts to incentivize state-sponsored workers to act like 
“spontaneous grassroots supporters” in online discussion boards. In contrast to Han’s study—
which found rigid work arrangements producing unnatural bot-like activity—Corpus Ong et al.’s 
research in the Philippines context [40] revealed how a hierarchized group of professional 
political operators used fake online personas in ways that emphasized individualization and 
flexibility to conduct an information operation. 

In our research, we analyze this phenomenon of coordinated impersonation within an online 
discourse or activist community from a CSCW perspective—considering this activity as a type of 
online “work” conducted by an information operator (or agent) in interaction with an online 
crowd. This lens allows us to conceptualize how this collective activity includes other 
collaborating agents as well as more sincere activists who may not recognize that they are 
interacting with political agents. It also allows us to reveal this work as an improvised 
performance that both reflects and shapes the discourse within which it is embedded. 

3 BACKGROUND  

Our initial data for this study was not collected with the advance intent of studying information 
operations in relation to the #BlackLivesMatter movement. Rather, the seed data for this research 
was collected to facilitate prior related work that studied this discourse to learn about how digital 
activists frame events and competing social movements [55]. Just weeks after publication of that 
work, we realized that the communities we had studied had been targeted for online information 



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operations. This motivated us to return to this dataset to better understand how the work of 
those information operators intersected with the activities of online activists within that 
conversation. 

3.1 Black Lives Matter and Blue Lives Matter Discourse in 2016 

As boyd, Wardle and others have argued [9, 65], the production of online propaganda cannot be 
understood in isolation from its social, political, technological, and cultural context. This research 
examines the production of online propaganda on Twitter in a context that intersects with issues 
of race, partisanship, gun violence, digital activism, and the failures of public institutions. 
Specifically, we investigate the activities of one set of actors in an online discourse about the 
#BlackLivesMatter movement and shootings in the U.S. during 2016.  

The hashtag #BlackLivesMatter was first coined in a Facebook post by Patrice Cullors and 
Alicia Garza in 2013 in response to the acquittal of George Zimmerman in the shooting death of 
Trayvon Martin [24]. The post and correspondingly the hashtag spread virally across social 
media platforms and crystallized in an on- and offline social movement that brought 
conversations on race into mainstream discourse, particularly shootings of African-American 
men by police officers. On their webpage, the BLM organizers describe BLM as "an ideological 
and political intervention in a world where Black lives are systematically and intentionally 
targeted for demise" [6]. Over time, a counter-movement took shape on social media, specifically 
critiquing the BLM movement for deprioritizing other lives (#AllLivesMatter) and being founded 
in a “false narrative” that vilifies police officers (#BlueLivesMatter) [7]. This counter-movement 
gained momentum in 2016, after shootings of police officers in Baton Rouge, Louisiana and 
Dallas, Texas prompted a spike in the volume of tweets related to counter-frames, for example 
about #BlackLivesMatter activists allegedly advocating for violence towards police [1, 55].  

3.2 Public Announcements Regarding Information Operations in 2017 

This discourse was also taking place during a time (2016) when Russian information operations 
in the US were particularly active, prior to the congressional investigations to highlight the 
problem [62, 63] and the actions taken by the social media companies to address it [60, 53]. In an 
April 2017 report, Facebook acknowledged that their platform had been used for “information 
operations” by both state (i.e. Russia) and non-state (i.e. Wikileaks-affiliated) actors to influence 
the 2016 U.S. Presidential election [66]. After Facebook’s announcement, representatives from 
other social media companies including Twitter, Tumblr, and Reddit also came forward to 
acknowledge that their platforms had been utilized for information operations by the previously 
mentioned Internet Research Agency (RU-IRA), an entity known to be a Russian ’troll farm’. 

In response to speculation surrounding the role of the RU-IRA in the 2016 presidential 
election, Twitter released a list of 2,752 RU-IRA affiliated troll accounts in November 2017 [62, 
63]. After identifying these accounts and presumably to protect other users from further 
deception, Twitter suspended the RU-IRA accounts, removing their account profile and tweet 
history from public view. This illustrates how social media content associated with clandestine 
activities can be challenging to gather and study due to its ephemerality. Our research team was 
able to overcome the ephemerality issue in this case because we had already curated, visualized, 
and intensely analyzed the relevant data described here. 

Since the release of the initial list, Twitter has announced the suspension of more RU-IRA 
accounts (although the details of these accounts have not been released) and investigative 
reporting has provided a clearer image of how RU-IRA troll accounts operated [51, 57]. These 
reports indicate that the RU-IRA employed carefully-vetted individuals with strong knowledge of 



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American pop culture and fluency in English to pose as Americans on social media and engage in 
conversations surrounding American social issues. Journalists have specifically noted that the 
online conversation around BlackLivesMatter and BlueLivesMatter was a significant point of 
access for these information operations [e.g. 51]. Though these industry reports and journalistic 
accounts provided rapid and needed insight, there is still a need to more systematically 
understand what these strategies are and how they interact with online discourse communities. 

4 METHODS 

Our interpretivist mixed-methods research iteratively analyzes our data by drawing on the 
guidelines and perspective of Charmaz’s constructivist grounded theory [11] to render a nuanced 
and flexible explanation of the activities enacted by RU-IRA affiliated Twitter accounts. 
Acknowledging the scale and multi-sited nature of the networked discourse in which we study 
these accounts, we extend methods for conducting research on large-scale, online social 
interactions [42, 19, 48, 27] and analyzing the spread of online misinformation [2, 34] during 
crisis events. We start by generating a network graph of retweets that reveals structurally 
distinct communities in the politicized discourse we are studying. This guides our inquiry by 
allowing us to harness structural data (behavioral network ties) to narrow down our case-
selection for in-depth qualitative research. We do this by cross-referencing a list of 2,752 
suspended RU-IRA affiliated accounts and systematically selecting the 29 accounts that were well 
integrated into the information network (the ‘who’). We then conduct a qualitative analysis 
through bottom-up open coding on the digital traces left by these accounts (i.e. tweets, profiles, 
linked content and websites), writing analytical memoes, and reflecting on the research process 
to consolidate observations of how they were participating in this discourse (the ‘what’). 
Juxtaposing these fragmented micro-level observations with the network graph—which 
illuminates the sub-networks these accounts were integrated with (the ‘where’)—helps us build 
up into a more macro-understanding of how these accounts worked to support an information 
operation. 

4.1 Data Collection and Filtering 

Our initial dataset consisted of 58.8M tweets that were posted and collected between December 
31st 2015 and October 5th 2016. We collected these tweets by tracking shooting-related keywords 
like “gun shot”, “gunman”, “shooter” and “shooting” using the Twitter Streaming API.  

We further filtered this set to tweets containing the terms “BlackLivesMatter”, 
“BlueLivesMatter”, or “AllLivesMatter” (“*LM”) in the text. The resulting dataset of 248,719 
tweets was used in prior work which established divergent and competing frames tied to the 
#BlackLivesMatter and #BlueLivesMatter hashtags [55]. This curated dataset—i.e. limited to *LM 
tweets with shooting terms—enabled us to explore the role played by RU-IRA affiliated accounts 
in a politically-charged online discussion related to activist movements and counter-movements 
in the U.S. in 2016. Importantly, this dataset is not representative of the broader BlackLivesMatter 
discourse but is focused on discourse related to violent offline events that included shootings of 
African Americans by police officers and shootings of police officers by an African American. 

To focus our investigation on accounts that demonstrated some level of sustained engagement 
and influence in the conversation, our final filtering step involved limiting our analysis to 
accounts with a retweet degree (sum of how many times an account was retweeted and how 
many times an account retweeted other accounts) greater than one. This final step produced 
22,020 accounts, who were responsible for 89,437 of the tweets in our “*LM” dataset. 



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4.2 Network Analysis 

We iteratively visualizated retweet flows between the 22,020 accounts by constructing a network 
graph (see Figures 1 and 2) in which we defined nodes to be Twitter accounts and directed edges 
to be retweets between accounts. We used the Force Atlas 2 layout in Gephi [4] to determine the 
visual layout of this graph. The retweet flows between these accounts consisted of 58,698 
retweets. To formalize structural observations of the network, we used the Infomap optimization 
of the map equation to systematically detect communities in the graph, ultimately producing two 
main communities (“clusters”) [15, 47]. We examined the effect of tuning Infomap parameters 
such as the inclusion of nested subclusters and overlapping modules; however, these did not 
significantly alter the extreme separation of the two main communities of the graph, and we thus 
ran the Infomap analysis specifying a directed graph with all other parameters at the default 
setting. To categorize and contextualize these clusters, we applied methods used in our prior 
work [55], examining the most frequently appearing hashtags in the account descriptions and 
supplementing this with the most-followed accounts in each cluster. This established that the two 
clusters could be categorized as roughly divided across American political lines (Right-leaning 
and Left-leaning). Finally, we located the RU-IRA accounts in the graph. More details on this 
process and its results are included in the Findings section.  

4.3 Identifying RU-IRA Accounts 

Having established the broader context of the retweet graph, we next looked for the RU-IRA 
accounts. To identify RU-IRA-affiliated accounts in this dataset, we relied on a list of 2,752 
suspended RU-IRA accounts released by Twitter in November 2017 as part of their testimony before 
the U.S. House of Representatives Permanent Select Committee on Intelligence [62 , 63].  

In the initial keyword-filtered dataset, cross-referencing with Twitter’s list revealed that 96 RU-
IRA accounts from Twitter’s list were present in the data—the subset of RU-IRA troll accounts who 
tweeted at least once with #BlackLivesMatter, #BlueLivesMatter, or #AllLivesMatter. After filtering 
by retweet degree and limiting to the two large communities as described above, the number of RU-
IRA accounts in our dataset was reduced to 29. We can summarize this subset as the RU-IRA 
accounts who participated via retweeting or being retweeted at least twice in the network. As 
described above, the purpose of this filtering was to find those accounts that were relatively well 
integrated into the information network, meaning that this subset of RU-IRA accounts generally 
interacted more with the network surrounding them. Though this limited the number of RU-IRA 
accounts we examined, it allowed us to focus our subsequent qualitative analysis on those accounts 
that likely had greater visibility and perhaps greater potential for influence within the network. 

4.4 Qualitative Analysis 

After examining the position of known RU-IRA accounts in relation to other accounts in the 
network, we began an analytic accounting of how these 29 accounts participated in *LM discourse. 
These accounts produced 109 tweets (retweeted 1,934 times) in our *LM collection, which we used 
as an initial sample in our qualitative inquiry. This data helped us develop some initial 
interpretations, but our constructivist grounded approach required further data collection via 
theoretical sampling to check, fill out and extend our theoretical categories.  

We therefore supplemented our analyses using data from the Internet Archive’s Wayback 
Machine, a free and open-source internet archive that save webpages [56] through a variety of web 
crawls being run by different programs. Searching this archive, we were able to manually retrieve 
234 timeline snapshots—including profile content as well as 4,682 tweets and retweets—for these 
accounts. While timelines for these accounts are not systematically preserved, this content provides 



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a window into the RU-IRA trolls’ digital presence in ways that mitigate the limitations of keyword 
sampling and thus complement our other data. The snapshots also allow us to see how each 
account presented itself, including elements like profile images that were otherwise unavailable 
since Twitter had suspended the account.  

We considered three main units of analysis (in addition to the network graph). First, we 
examined profile data—i.e. the display pictures, background images and profile descriptions of the 
RU-IRA accounts. Second, we considered tweets with a focus on the original content produced by 
these accounts, including embedded images such as memes. We also paid close attention to cases in 
which these accounts retweeted each other. Third, we considered the external websites, social 
platforms and news articles these accounts linked to in an effort to “follow the person” [35] to attain 
a more holistic understanding of the disinformation campaign we were studying. 

Each of these types of data was examined, segmented and summarized through an initial round 
of open coding. Our codes focused on actions visible in the data and leveraged our prior contextual 
knowledge from having studied this particular #BlackLivesMatter-related discourse. These initial 
codes which fragmented the data were then drawn together through analytical memoing and 
clustering to form themes and categories. 

4.5 Methodological Challenges 

This study confronted three main methodological challenges that must be understood to interpret 
our findings correctly. First, the seed Twitter data we used to generate our network graph is both 
incomplete (due to rate limits) and biased (because of the shooting related terms we tracked). As a 
result, our findings are not intended to be representative of the overall #BlackLivesMatter 
conversation. Rather, we have a portion of a particular online discourse that invokes the movement 
in conjunction with incidents of violence during 2016. Similarly, due to the incomplete nature of 
our data, we cannot and do not seek to quantitatively assess the impact RU-IRA activities and 
contributions had on even this one discourse. Our goal is to understand how RU-IRA content was 
designed to interact with this discourse—which we already understand to be polarized and made up 
of a heterogenous web of actors who are speaking to different interests and values.  

Second, it is important to note that the identification and suspension of RU-IRA affiliated 
accounts is likely part of an evolving and ongoing effort at social media companies. We do not have 
access to Twitter’s methodology for identifying these accounts, but we do know that at least one of 
the 2,752 accounts was revealed to be a false positive (i.e. unaffiliated with the Internet Research 
Agency) [38]. Moreover, Twitter has identified additional RU-IRA accounts since the release of this 
initial list [60] but has not made information on these accounts publicly available to our knowledge. 
Independently, we have tracked more accounts being suspended in both clusters—but particularly 
on the right—since we conducted this analysis (although we cannot infer that these accounts were 
RU-IRA affiliated). Consequently, we wish to caution readers from drawing any false equivalencies 
from the fact that we located and subsequently examined 22 RU-IRA accounts in the left-leaning 
cluster and 7 in the right-leaning cluster.  

Third, despite the generally presumed persistence of social media content, the content associated 
with clandestine activities is prone to ephemerality, creating challenges for research [17, 50]. Our 
multi-sited research approach—using of Internet Archive data, examining linked websites and 
considering the activities of these accounts on other social platforms—attempts to address these 
challenges by acknowledging that information operations on these platforms are interconnected 
and interrelated activities. 

 



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5 FINDINGS 

5.1 Structural Analysis: Positioning Across Political Lines  

We now return to the accounts in the dataset identified in section 4.1 which both tweeted with an 
*LM keyword and were well-integrated into the retweet network. Figure 1 illustrates each step of 
our analysis of the information flow graph, where the 22,020 Twitter accounts are nodes and the 
58,698 retweets between these accounts are directed edges. In our first step, we visualized the 
structure of the graph, noting that the majority of nodes are concentrated in two relatively distinct 
clusters. This observation suggests homophily in the accounts retweeting each other. To solidify 
this, our next step was to use a community detection algorithm to systematically identify clusters. 
Specifically, we used the Infomap algorithm, an optimization of the Map Equation that assigns 
nodes to a community using a greedy algorithm that optimizes flow (in this case retweets) between 
nodes. The results of this step supported our earlier observation of structural homophily: 91.7% 
(20,192) of the nodes are grouped in two large clusters in the center of graph containing 48.5% and 
43.2% of the nodes. We focus our remaining investigation on these two clusters (colored pink and 
green in Figure 1). 

Our final step was to understand who was in the clusters. To do this, we used salient account 
characteristics—the top 10 hashtags in the accounts’ profile descriptions as well as the most-
retweeted accounts by cluster—to classify and contrast the two clusters (shown in Table 1). In both 
clusters, the number of accounts with a hashtag in the user description ranged from 31.6% to 34.2%. 
This analysis revealed that our graph was roughly divided along political lines. The most frequently 
occurring hashtags in the pink community bios were #BlackLivesMatter, #ImWithHer (expressing 
support for Democratic presidential candidate Hillary Clinton), and #BLM (a shortening of 
#BlackLivesMatter). #BlackLivesMatter is the top hashtag by a significant amount. We also see that 
left-leaning journalist and activist @ShaunKing and pro-BLM news account @trueblacknews are in 
the top ten most-retweeted accounts of this community. Therefore, we categorize this cluster as 
broadly Left-leaning on the U.S. political spectrum. In contrast, the most frequent hashtags in the 
green community were #Trump2016, #MAGA, and #2A, where #Trump2016 and #MAGA indicate 
support for Republican presidential candidate Donald Trump and #2A indicates support for the 
right of private citizens to own guns. Nearly 7% of the accounts in this cluster had #Trump2016 in 
their user descriptions. We categorize this cluster as broadly Right-leaning on the U.S. political 
spectrum. Building upon previous work [55], we infer that these two communities held divergent 
and competing frames surrounding officer-involved shootings and the Black Lives Matter and Blue 
Lives Matter movements. 

Next, we identify accounts from within our data that were associated with the RU-IRA and 
examine their location within the retweet network graph. In total, there were 96 RU-IRA accounts 
within our dataset but only 29 of these appeared in our retweet network graph (limited to accounts 
with a retweet degree of at least two and within the two clusters). 22 of these accounts were in the 
left (pro-BLM) cluster and 7 of these accounts were in the right (anti-BLM) cluster. 

 

Fig. 1. From left to right: using Force Atlas 2 to visualize retweet flows, identifying clusters with 
Infomap, and using cluster characteristics to label communities 



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Table 1. Overview of Accounts in the Two Clusters 

Color Top 10 hashtags in account descriptions Number of 
accounts 

Top 10 accounts by retweet 
count 

Pink blacklivesmatter (8.529%), imwithher (1.442%), 
blm (1.105%), uniteblue (1.039%), feelthebern 

(1.021%), allblacklivesmatter (0.721%), 

bernieorbust (0.599%), neverhillary (0.571%), 
nevertrump (0.571%), freepalestine (0.524%) 

10681 trueblacknews (3773), 
YaraShahidi (2108), 

ShaunKing (1553), 

ShaunPJohn (1214), 

BleepThePolice (692), 

Crystal1Johnson (573), 

DrJillStein (524), meakoopa 

(409), kharyp (387), tattedpoc 
(307) 

Green trump2016 (6.615%), maga (6.099%), 2a (5.237%), 

tcot (2.787%), trump (2.776%), neverhillary 

(2.524%), makeamericagreatagain (2.461%), nra 

(2.229%), trumptrain (1.998%), bluelivesmatter 
(1.872%) 

9509 PrisonPlanet (4945), 

Cernovich (1704), 

LindaSuhler (1034), 

MarkDice (789), DrMartyFox 

(758), _Makada_- (591), 

andieiamwhoiam (510), 

LodiSilverado (500), 
BlkMan4Trump (458), 
JaredWyand (447) 

 
These 29 accounts also demonstrated a wide range of engagement: @BleepThePolice was 

retweeted 692 times by 614 distinct accounts on our graph while six RU-IRA accounts were not 
retweeted at all. The top-ten most prominent RU-IRA accounts by retweet count—such as 
@BleepThePolice, @Crystal1Johnson, and @BlackNewsOutlet on the left and @SouthLoneStar, 
@TEN_GOP, and @Pamela_Moore13 on the right—are highlighted in Table 2. Cross-referencing 
Tables 1 and 2, we note that in the left cluster, two RU-IRA accounts (@BleepThePolice and 
@Crystal1Johnson) are among the left cluster’s most-retweeted accounts. 

Figure 2 highlights the trajectories of retweets of RU-IRA accounts (orange) in the rest of the 
graph (blue). Of the 58,698 total retweet edges on the graph, 1,960 (3.33%) were retweets of RU-IRA 
accounts. We do not attempt to tackle the question of the influence of RU-IRA accounts with this 
graph, but rather to illustrate their position in the ecosystem. While we cannot speak to their 
impact, we can use this graph to examine where their content circulated and, in tandem with 
qualitative analysis, identify their tactics and apparent coordination practices and situate these 
within our current knowledge of information operations. 

An initial—and striking—observation is that there were clearly RU-IRA accounts embedded in 
both clusters, meaning that RU-IRA content was retweeted on both “sides” of the conversation. 
Furthermore, we can see that while RU-IRA content spread throughout each community—and in 
some cases was relatively highly retweeted—it very rarely moved between them. Informed by prior 
work examining divergent framing [55], this suggests an effort by the RU-IRA to purposefully 
embed themselves in two distinct communities on either side of a highly charged framing conflict. 
 



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Table 2. Prominent RU-IRA Accounts Ordered by Cluster and Number of Retweets 

 

 

Fig. 2. Highlighting retweets of known RU-IRA accounts (orange) compared to retweets of the rest 
of the graph (blue). 

We can summarize these findings by stating that while RU-IRA content was clearly broadcast 
to both clusters, the RU-IRA content that circulated in each cluster originated from two distinct 
groups of RU-IRA accounts. With the inference that these communities hold oppositional and 
incompatible beliefs surrounding officer-involved shootings and race, this suggests that the RU-
IRA accounts tailored content to each community. This aligns with previous literature claiming 
that current disinformation tactics are ideologically fluid and seek to exploit social divides [43, 
45].  

We also note that that while the presence of orange nodes and edges appears larger in the left-
leaning cluster, the limitation of our original dataset and the curated list of RU-IRA accounts 
provided by Twitter prevent any quantitative comparisons between the two sides. In other 
words, this graph provides a window into RU-IRA activity and patterns but does not determine 
relative impact. 

Handle Cluster (Left 
or Right) 

Number of Tweets 
in Dataset 

Number of Retweets 
in Cluster 

Follower Count 

@BleepThePolice L 18 692 11,926 

@Crystal1Johnson L 14 573 16,510 

@BlackNewsOutlet L 2 60 4,723 

@gloed_up L 15 53 17,876 

@BlackToLive L 2 47 7,072 

@nj_blacknews L 2 35 1,992 

@blackmattersus L 2 34 5,841 

@SouthLoneStar R 2 225 15,612 

@TEN_GOP R 1 45 18,451 

@Pamela_Moore13 R 1 23 9,289 



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5.2 Production of Inauthentic Identities 

Our network analysis reveals that RU-IRA affiliated accounts interacted with two different 
networked audiences in this large-scale discourse (politically left leaning and right leaning). For 
the remainder of our analysis we will focus on the orange nodes in Figure 2 to understand the 
nature of these interactions and how these accounts adapted to fit within the two structurally 
distinct communities. We begin by considering how these accounts presented themselves. This 
helps us understand how processes of feigning authenticity have evolved and adapted to social 
media environments, which contain less static and more user-driven content production and a 
networked architecture that blurs the lines between contexts like entertainment and news 
consumption. This also helps us triangulate the extent to which the RU-IRA accounts in Figure 2 
intentionally targeted different audiences, since how the operators of these accounts attempted to 
portray themselves reflects their imagined audience [33, 36]—i.e. the mental pictures people 
construct about others to guide self-presentation. Just as writers imagine media audiences 
appropriate to their topic and form, and use textual cues to invoke those audiences into being 
[41], the differences and similarities across RU-IRA profiles reveals who these accounts were 
attempting to write to and deceive. 

5.2.1 Profiles:  Like many other social media participants, RU-IRA affiliated Twitter accounts 
constructed user profiles to portray both an interesting and authentic self. These profiles were 
reproduced on other platforms like Facebook and Tumblr, suggesting an effort to build and 
maintain consistent online personas. 

 We observed four systematic patterns of forged profiles. The first two were the establishment 
of ‘the proud African American’ as a political identity, on the one hand, and the articulation of 
‘the proud White Conservative’, on the other. These two patterns consisted of accounts that 
presented themselves as the personal Twitter accounts of real and ordinary citizens within their 
communities. These accounts used cultural, linguistic, and identity markers in their Twitter 
profiles to align themselves with the shared values and norms of either the left- or right-leaning 
clusters. For instance, accounts in the left-leaning cluster that fell in this category consistently 
used display pictures to present themselves as African Americans coming from locations such as 
Chicago, New Jersey, and Richmond, Virginia with profile descriptions such as:  

@TrayneshaCole: Love for all my people of Melanin. Your BLACK is BEAUTIFUL! 

#MyPussyMyChoice #BlackGirlsMagic #BlackLivesMatter 

@Crystal1Johnson: It is our responsibility to promote the positive things 

that happen in our communities. 

@4MySquad: no black person is ugly #BlackLivesMatter #StayWoke 

Accounts in the right-leaning cluster tended to use photographs to present themselves as 
white men and women living in Texas or other southern states who were interested in firearms 
and the right to bear them, using profile descriptions like:  

@TheFoundingSon: Business Owner, Proud Father, Conservative, Christian, 

Patriot, Gun rights, Politically Incorrect. Love my country and my family 

#2A #GOP #tcot #WakeUpAmerica 

@Pamela_Moore13: Southern. Conservative. Pro God. Anti Racism 

@USA_Gunslinger: They won't deny us our defense! Whether you're agree with 

me or not, you're welcome here! If you don't want to be welcomed, go f*ck 

yourself. 



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These profiles can appear to be the online personas of real African and White Americans 
because they appeal to creative self-expression and caring for others. Another part of what can 
make these personas intuitively ‘fit’ comes from how they invoke stereotypical thinking by 
articulating African and White Americans as binary groups that are internally homogenous with 
respect to politics. In the past, such dichotomizations have been directly and indirectly 
constructed by media portrayals elsewhere [13, 14]. But by exploiting the participatory and 
interactive nature of social media, imaginary others can be brought to life in new ways by 
information operations in order to sustain and amplify these dichotomizations [18]. 

The third and fourth patterns mirrored the first two, but enacted organizational accounts for 
grassroots political and media groups from these respective “sides”. For instance, accounts in the 
right-leaning cluster adopted names like @tpartynews, using a "Tea Party" teapot logo in the 
colors of the American flag and acting as a conservative news source. Similarly. @TEN_GOP, a 
well-known RU-IRA affiliated account [23] that appeared in our dataset, described itself as the 
“Unofficial Twitter of Tennessee Republicans. Covering breaking news, national politics, foreign 
policy and more. #MAGA #2A”. In the left-leaning cluster, these accounts presented themselves 
as alternative media sources for racial justice. These accounts emphasized localness, frustration 
with mainstream media, and crowd participation, respectively, with profile descriptions like: 

@nj_blacknews: Latest and most important news about New Jersey black 

community 

@Blackmattersus: I didn't believe the media so I became one. 

@BlackToLive: We want equality and justice! And we need you to help us. 

Join our team and write your own articles! DM us or send an email: 

BlackToLive@gmail.com 

These accounts often linked back to their own websites, which suggests an attempt to 
undermine traditional media in favor of alternative media websites that might have been setup to 
support the information operation. For instance, the account @dontshootcom links to the domain 
donotshoot.us, which describes itself as a tool for empowering grassroots activists: 

“Don’t Shoot is a community site where you can find recent videos of outrageous police misconducts, 
really valuable ones but underrepresented by mass media. We provide you with first-hand stories and 
diverse videos. Our mission is to improve the situation in the US and the lives of its citizens, to do our 
best to help end inhumane and biased acts. We are here to empower you, give you a voice and help you 
get justice with all our might.” 

Figure 3 summarizes how RU-IRA accounts used profile display pictures to foster identities 
that could attract and command attention from audiences with different political alignments and 
news consumption habits. Viewing these images collectively in this manner reveals both 
convergence and divergence in the production dynamics governing how these identities were 
crafted. The consistent and similar nature of these fake identities (within any one of the single 
‘quadrants’ below) suggests convergence: that perhaps a common script, manual or ‘brand bible’ 
[40] may have been used to delineate the political stances, social background and personality 
traits of these accounts. Ensuring this kind of brand or identity consistency aligns with 
professional practices of micro-targeting in marketing and American political campaigning that 
have evolved to take advantage of the capabilities of social media platforms [39]. 



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Fig. 3. Display pictures of RU-IRA accounts arranged by categories. 

Simultaneously, the differences in these identities (between the left/right or upper/lower sides 
of Figure 3) suggests efforts to engage in audience segmentation and having multiple audience 
touchpoints. For instance, by delivering either a personal identity or a more organizational one, 
RU-IRA accounts collectively took advantage of how social contexts ‘collapse’ together on sites 
like Twitter to promote messages to audiences through different points of access. Researchers 
have noted that trying to balance these contexts through a single account opens the possibility of 
appearing inauthentic to one’s followers [36]—a risk the RU-IRA mitigated by having accounts 
specialize in different roles. 

5.2.2 Tweets:  Beyond creating a fake profile, the RU-IRA accounts produced tweets 
containing commentary, images, news and videos that helped shape, reproduce and solidify the 
political identities they enacted. RU-IRA accounts with both ‘personal’ and ‘organizational’ 
profiles in the left-leaning cluster frequently tweeted to uphold the accomplishments and culture 
of African Americans and share positive feelings around the Black Lives Matter movement. For 
instance, @Crystal1Johnson maintained a pinned tweet about how Muhammad Ali’s Hollywood 
Walk of Fame Star is unique for ‘hanging on a wall, not for anyone to step on’ and actively 
celebrated Black History Month by tweeting regularly about topics like African American 
women’s hairstyles and accomplishments in education. Similarly, accounts like @TrayneshaCole, 
@gloed_up, @BlackToLive, @RobertEbonyKing and @BlackNewsOutlet tweeted in support of 
entrepreneurship projects by African Americans and locating missing Black persons. The 
expression of personal opinions on events, and the use of humor and entertainment also featured 
prominently as these accounts also tweeted about music by African American artists and joked 
around movies like Black Panther and Hidden Figures in which African Americans played 
prominent roles. 

Similarly, accounts in the right leaning cluster tweeted to celebrate traditional American 
holidays, the American flag, and military service. For instance, @TheFoundingSon maintained a 
pinned tweet for #PearlHarborRemembranceDay as “a reminder to the rest of the world that 



Acting the Part: Examining Information Operations Within #BlackLivesMatter Discourse  20:15 
 

Proceedings of the ACM on Human-Computer Interaction, Vol. 2, No. CSCW, Article 20, Publication date: November 2018. 

American people cannot be easily broken”. Similarly, @SouthLoneStar also pinned a tweet that 
told the personal story of “Nick [who] was paralyzed by an IED in Afghanistan. Wendy met him 
in VA hospital and became his caregiver full-time. Now these 2 heroes are married”. Moreover, 
just as left-leaning RU-IRA accounts tweeted about certain movies and occasions like Black 
History Month, these accounts made it a point to celebrate traditional American holidays like 
Thanksgiving and Easter while commenting on television shows with hashtags like 
#TheWalkingDead. Another example from @SouthLoneStar is illustrative here:  

“Today is National Peace Officer Memorial Day. We honor those that paid the 

ultimate sacrifice #BlueLivesMatter” 

Other accounts like @USA_Gunslinger and @KarenParker93 followed similar patterns and 
used hashtags like #WednesdayWisdom to tweet pictures of snowmen holding up an American 
flag (see Figure 4) and children pretending to be police officers.  

 

Fig. 4. Sample tweets circulated by RU-IRA accounts in separate clusters to cultivate trust. 

These examples highlight how information operations can invoke content that is not always 
amenable to fact-checking nor straightforward to problematize. The activities of these accounts 
included not only acts of ‘rational’ political persuasion like presenting arguments and true or 
false claims. They also involved representing and affirming the personal experiences, shared 
beliefs and cultural narratives of their audiences. This could help these accounts blend into the 
communities they targeted, and it could also help them tap into the social and emotional 
literacies that often guide people’s engagement with the public sphere. 

Although the consistency of this content speaks to a certain level of rigid arrangements (e.g. 
accounts on the left ought to celebrate Black History Month), the content also serves to illustrate 
a level of spontaneity. For instance, multiple accounts demonstrated the ability to understand the 
nuances of American pop-culture and creatively adapt to trending topics to ‘build their brand’ 
(e.g. opining about movies, music and television shows). Aligning with investigative interviews 
with former RU-IRA employees [57], we would suggest that these dynamic behaviors are a signal 
that these accounts were not fully automated bots—and that the workers operating these 
accounts had at least some agency to “improvise” as part of their work. 

5.2.3 Coordination to Build Trust:  On social media, interacting with streams of user-generated 
content produced by one’s personal network is central to exhibiting ‘evolving connectivity’ [44] 
and cultivating trust [17]. We did not observe explicit interaction between RU-IRA accounts 
when they were in different clusters, but we did observe accounts from within the same cluster 
mentioning and retweeting each other over a variety of topics. For instance, for a researcher 
reading their content, the users @gloed_up, @BleepThePolice and @TrayneshaCole gave the 



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impression that they were part of a social clique. Their occasional, casual interactions projected 
authenticity while also enabling them to better manage their audience’s attention by generating 
‘buzz’ around certain topics such as protests or other news items. Figure 5 below furnishes an 
example that succinctly captures the flavor of interactions between these accounts.  

 

Fig. 5. Three RU-IRA accounts retweeting each other. 

In this example, @BleepThePolice tweeted out a graphical meme touting “Girl Power”, 
celebrating the march and asking if anyone is attending, perhaps with the goal of getting 
responses—and therefore engagement—from that account’s audiences. @TrayneshaCole answers 
that call with a tweeted reply message pleading for black men to get more involved in women’s 
rights. Later, @gloed_up—whose screen name is 1-800-WOKE-AF—retweets both tweets. This 
example shows the three RU-IRA accounts interacting with each other to create the illusion of 
organic engagement. 

Retweet flows provide an incomplete picture of how RU-IRA accounts supported each other’s 
activities. A richer window into understanding how the RU-IRA coordinated and provided 
mutual support to each other to appear as authentic activists and influencers comes from 
@BlackMattersUS. A website associated with this account was promoted on Twitter by 
@Crystal1Johnson, and the site in turn credits Crystal Johnson as a writer who interned at NBC: 

“Crystal Johnson has been with Black Matters since October 2014. Her passion is giving voice to the 
community. During her undergrad, Crystal took an internship with the local NBC affiliate WEYI. In 
2014 she moved to Atlanta to help start a new project called BlackMatters. She is among the most active 
members of BlackMatters.” 

Aligning with journalistic investigations by Craig Silverman [51], we also observed that 
@BlackMattersUS took the step of creating and promoting multiple meetups, possibly to create 
links—or project the illusion of having links—with real, local organizing groups. These meetup 
related efforts were also supported by accounts like @Crystal1Johnson who recruited volunteers 
and @Blacktivists who set up a ‘Black Unity March’. 

@BlackMattersUs: If you are against #policebrutality #racism #incarceration 

#oppression take part in #BlackLivesMatterMarch <link to meetup> 

@BlackMattersUs: Support Black Owned Small business at this one stop shop 

expo event!!! #BLM #BlackLivesMatter <link to meetup>  



Acting the Part: Examining Information Operations Within #BlackLivesMatter Discourse  20:17 
 

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@Crystal1Johnson: We’re looking for good people who are ready to help us in 

organizing events around the country. DM for more info 

The BlackMattersUS website also put together a podcast on SoundCloud called ‘SKWAD 55’ to 
‘gather strong Black voices’1, which was promoted by accounts like @4MySquad which 
positioned themselves as interested in rap music. These examples illustrate how RU-IRA accounts 
collaborated to feign legitimacy via multiple channels and platforms. 

5.3 RU-IRA Participation in #*LM Discourse 

We have described how RU-IRA accounts carefully constructed fictitious identities as people and 
organizations with ethno-cultural backgrounds that systematically shifted depending on whether 
the account was embedded within the politically left- or right-leaning cluster. In this section we 
will summarize RU-IRA content related to #BlackLivesMatter, #BlueLivesMatter and 
#AllLivesMatter. We organize this content into three different patterns to show how a seemingly 
diffuse set of individual actors on social media worked together to amplify certain messages.  

5.3.1 Modeling the ‘anti-Police’ #BlackLivesMatter protestor:  Each RU-IRA account that we 
examined in the left-leaning cluster connected their African-American identity to being a 
#BlackLivesMatter activist by tweeting extensively about police officers shooting unarmed 
African American men and women, including disabled persons and minors. These tweets 
frequently linked to stories from established media sources2 such as Fox News and the New York 
Times but also alternative media sources3 including conspiracy theory and RU-IRA affiliated sites 
such as TheFreeThoughtProject and BlackMattersUS. The process of mixing ‘traditional’ and 
alternative media sources into a single content stream is notable because it can elevate the image 
and content of the more alternative sites, particularly for audiences that skim headlines to cope 
with high volumes of information. 

These accounts also used their political identities of African-American #BlackLivesMatter 
activists to model an exuberant anti-police stance via tweets, profile background images, and 
occasionally account names. Accounts like @Bleepthepolice, @gloed_up, and @4mysquad 
combined hashtags like #BLM and #BlackLivesMatter, with #ACAB (short for all cops are 
bastards), #Amerikkka, #BadCop, #BleepThePolice, #CowardCops, #HateIt, #KillerCops and #riot: 

@4MySquad: they don't hire anyone with an iq of over 100' #StayWoke #Police 

#dumb #AllCopsAreBad #ACAB 

@GloedUp: French #police are too corrupt, incompetent to fight terrorism 

#BlackTwitter #BlackToLive #BlackLivesMatter #acab 

@Crystal1Johnson: Blue’s a job, that shit don’t matter! #BlackLivesMatter! 

                                                           

1 https://blackmattersus.com/15026-meet-the-first-skwad-55-podcast/  
 https://soundcloud.com/skwad55  

2 http://www.foxnews.com/us/2016/06/28/chicago-police-to-take-second-look-at-deadly-shooting-teen-with-antique-
gun.html 
https://www.nytimes.com/2016/10/13/nyregion/10-black-employees-at-new-york-fire-dept-cite-bias.html 
3 https://thefreethoughtproject.com/disturbing-video-shows-cops-shoot-suspect-walk-hostage-put-4-rounds/ 
 https://blackmattersus.com/17023-major-mismatches-in-the-story-of-white-cop-raping-15-yo-black-girl/ 

https://web.archive.org/web/20150904055426/https:/twitter.com/hashtag/StayWoke?src=hash
https://web.archive.org/web/20150904055426/https:/twitter.com/hashtag/Police?src=hash
https://web.archive.org/web/20150904055426/https:/twitter.com/hashtag/dumb?src=hash
https://web.archive.org/web/20150904055426/https:/twitter.com/hashtag/dumb?src=hash
https://web.archive.org/web/20150904055426/https:/twitter.com/hashtag/AllCopsAreBad?src=hash
https://web.archive.org/web/20150904055426/https:/twitter.com/hashtag/ACAB?src=hash
https://blackmattersus.com/15026-meet-the-first-skwad-55-podcast/
https://soundcloud.com/skwad55
http://www.foxnews.com/us/2016/06/28/chicago-police-to-take-second-look-at-deadly-shooting-teen-with-antique-gun.html
http://www.foxnews.com/us/2016/06/28/chicago-police-to-take-second-look-at-deadly-shooting-teen-with-antique-gun.html
https://www.nytimes.com/2016/10/13/nyregion/10-black-employees-at-new-york-fire-dept-cite-bias.html
https://thefreethoughtproject.com/disturbing-video-shows-cops-shoot-suspect-walk-hostage-put-4-rounds/
https://blackmattersus.com/17023-major-mismatches-in-the-story-of-white-cop-raping-15-yo-black-girl/


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Fig. 6. Example memes circulated by RU-IRA accounts in the left cluster. 

Figure 6 above also illustrates how the memes these accounts presented favored an 
uncompromising and adversarial stance towards law enforcement. The use of these charged 
messages and vocabulary of hashtags in conjunction with the central political tag of 
#BlackLivesMatter suggests an attempt by RU-IRA accounts to connect with both existing 
discontent and amplify it by proliferating certain meanings around the #BlackLivesMatter tag—
similar to the phenomenon of hashtag drift [8].  

This activity feeds directly into attempts to frame #BlackLivesMatter as an anti-police hate-
group. From prior research [55] we know that such framings were actively resisted and addressed 
by #BlackLivesMatter activists4 while being proliferated within anti-BlackLivesMatter discourse. 
By tapping into this larger reservoir of antagonistic discourses proliferating in American politics, 
these accounts amplified toxicity in public discussions. This is further supported by how these 
accounts invoked the competing hashtags #BlueLivesMatter and #AllLivesMatter to attack them. 
‘Calling out’ these hashtags illustrates how these accounts did not just speak to the communities 
that they were pretending to be a part of, but also aimed to communicate an antagonistic 
representation of those communities to others. 

@BleepThePolice: #BlueLivesMatter is BS 

@TrayneshaCole: And y’all not saying #AllLivesMatter when y’all are 

shooting up schools now are you? 

Finally, it is significant that not all of the stories about police misconduct that were circulated 
by these accounts were verified or grounded in fact. One notable example in our data that 
highlights the creativity of these accounts, and which has been decisively debunked elsewhere 
[51], relates to @4mysquad circulating gifs with the description “Shocking video shows Black 
teenage girl being sexually assaulted by NYPD officer.” These gifs were framed as surveillance 
video footage showing a black teenager being assaulted by a white police officer, and they were 
also presented on @4mysquad’s Tumblr account. Following these gifs going viral, members of 
the online crowd began to refute and debunk this story. At this point BlackMattersUS tweeted 

                                                           

4 See: http://blacklivesmattervermont.com/wp-content/uploads/2017/01/FAQ.pdf as an example  

http://blacklivesmattervermont.com/wp-content/uploads/2017/01/FAQ.pdf


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and published a website article that linked to the gifs and attempted to refute the corrections5 
[51]. @4mysquad ultimately went on to issue an apology, stating: 

 “it was absolutely insensitive of me to make those gifs. I was furious and stoned...originally I’ve got dis 
anonymous message asking me to make a post…” [51] 

This example represents a creative and intentional attempt to inject false information into the 
#BlackLivesMatter discourse. The apology suggests again that these accounts were not fully 
automated ‘throw-away’ bots since they were managing their ‘brand’, disguise, and audience by 
monitoring and responding to feedback. The involvement of the BlackMattersUS website 
illustrates how RU-IRA accounts worked to sow anger and confusion over multiple channels and 
platforms. Examined as a two-part act, the video incident functioned both to further stoke anti-
police sentiments on the left and, once it was debunked, increase anti-BlackLivesMatter 
sentiments on the right. 

5.3.2 Promoting anti-BlackLivesMatter discourse:  Diverging from their counterparts, RU-IRA 
accounts in the right leaning cluster tweeted to both support #BlueLivesMatter and 
#AllLivesMatter and denigrate #BlackLivesMatter. These tweets delegitimized the 
#BlackLivesMatter movement by equating the meaning of the movement with propaganda and 
anti-police activities. @tpartynews and @TEN_GOP, for instance, engaged in this type of 
framing by tweeting out stories around the 2016 Baton Rouge and Dallas shootings of police 
officers with titles like “Mother of police shooting suspect blames #BlackLivesMatter”, and 
“WATCH: #BlackLivesMatter supporters interrupt a moment of silence for fallen police officers!” 
The personal category of RU-IRA accounts in this cluster also attacked #BlackLivesMatter more 
directly. 

@Pamela_Moore13: Black Lives Matter is a political construct, a hateful 

destructive ideology. It’s never been about black life. 

@KarenParker93: RT: If U Point A Gun At A Cop & Get Shot, Who’s Stupid 

#BlueLivesMatter 

@TheFoundingSon: Black man intentionally drives through 3 cops. That is 

hate that #BLM and Obama created #BlueLivesMatter 

The additional examples provided in Figure 7 also highlight how these accounts made heavy 
use of aggressive memes and images. Overall, these tweets play a complementary role with the 
content RU-IRA accounts were propagating in the left leaning cluster. Supporters and followers 
of the #BlackLivesMatter hashtag could potentially see this charged content and use it in forming 
their perceptions of others and the possibility of civil dialogue. Simultaneously, critics of the 
#BlackLivesMatter movement could see RU-IRA content that focused more on attacking police 
and less on the movement’s core messages. Both groups of users were also being selectively 
presented with news and information from these accounts that possibly played to pre-existing 
beliefs and biases (e.g. #BlackLivesMatter affiliated protesters behaving as looters and executing 
police officers / police officers sexually assaulting black citizens). In summary, RU-IRA accounts 
were acting as both information distributors and antagonistic stereotypes of ethno-cultural 
others. 

                                                           

5 https://blackmattersus.com/17023-major-mismatches-in-the-story-of-white-cop-raping-15-yo-black-girl/  

https://blackmattersus.com/17023-major-mismatches-in-the-story-of-white-cop-raping-15-yo-black-girl/


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Fig. 7. RU-IRA content about #BlackLivesMatter in right-leaning cluster. 

5.3.3 Converging to attack the ‘mainstream’ media:  RU-IRA accounts in both clusters 
converged by using #BlackLivesMatter discourse and their constructed political identities to 
criticize the ‘mainstream media’. The @BlackmattersUS profile description and website slogan of 
“I didn’t believe the media so I became one” effectively summarizes this message, which was also 
carried forward by personal style RU-IRA accounts on the left. These accounts mixed content that 
A) expressed frustration with how older traditional media institutions cover issues like officer 
related shootings and the #BlackLivesMatter movement itself; and B) equated these long-standing 
institutions with tools of oppression. Figure 8 illustrates more and less direct versions of this 
message. The second tweet in this example shows @BleepThePolice (boosted by another RU-IRA 
account) repurposing a message by @ShaunKing to hold up social media as a viable alternative to 
“the media”. 

 

Fig. 8. Examples of ‘left’ RU-IRA tweets criticizing traditional media. 

RU-IRA accounts in the right-leaning cluster echoed their counterparts in the left cluster using 
hashtags like #FakeNews, #WeAreTheMedia, #WakeUpAmerica and #CNNisISIS. “Propaganda is 
everywhere”, warned one account, after sending out a series of tweets criticizing mainstream 
media outlets for being the partisan mouthpieces of a corrupt global elite. The examples in Figure 
9 illustrate how the RU-IRA accounts took advantage of the fragmented media landscape in the 
U.S. by framing traditional outlets for being irrelevant distractions. Accounts in this cluster 



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further appropriated #BlackLivesMatter as a vector for such messages by linking the movement 
to globalist conspiracies. 

@Pamela_Moore13: If we don’t stop George Soros now, he will continue to 

drive divisive race baiting MSM narratives & riots to undermine Trump! 

#LockHimUp 

@TheFoundingSon: While the NYT tells you how Soros fights hate crimes his 

agenda incites hate towards police officers which results in tragedies 

#KeithScott 

 

Fig. 9. Examples of ‘right’ RU-IRA tweets criticizing traditional media. 

In summary, RU-IRA accounts among both the left and right leaning clusters converged to 
position traditional media outlets as institutions which manufacture a false reality for masses of 
people. This aligns with previous speculations [45] suggesting that undermining trust in 
established media sources can be a characteristic of disinformation, with the end goal of further 
destabilizing democratic discourse. 

6 DISCUSSION 

6.1 Information Operations as Collaborative Improvisations  

Information operations—including political propaganda, disinformation, and other forms of 
manipulation—on online platforms are a growing concern for political officials, platform 
designers, and the public at large. Journalists, intelligence professionals, and researchers from 
diverse fields are converging to examine this phenomenon. In this paper, we analyze an extended 
campaign of information operations from a CSCW perspective, applying a methodological 
approach that emerged from research on online interactions and collaborations in crisis events 
[42, 54, 34] to examine these operations not simply as messages broadcast to audiences, but as 
interactions between an account operator and their audience—or, more fittingly, as a 
performance by one or more actors, on and through multiple social media accounts, from within 
and in interaction with an online community. Our research suggests that these performances are 
not simply automated or even scripted, but are instead like an improvisation in the sense that an 
actor is given a set of constraints, but then dynamically adapts their performance in interaction 
with the crowd.  

Considering the limits of our data, we cannot see how this work is explicitly coordinated 
within the Internet Research Agency itself, but from our perspective we can see how the 



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accounts enact particular kinds of online personas, how they interact with each other in the 
online sphere, and, to some extent, how they interact with the online communities that they 
infiltrated. This view allows us, both as researchers and as people who participate in these online 
conversations, to better understand these tactics, revealing some of the mechanisms they use to 
manipulate people and what some of their larger goals are, in terms of shaping online political 
discourse (specifically in the United States). It also illuminates some of the challenges that social 
media platforms face in attempting to defend against these operations. 

6.2 Nurturing Division: Enacting Caricatures of Political Partisan Accounts  
Our findings show RU-IRA agents utilizing Twitter and other online platforms to infiltrate 
politically active online communities. Rather than transgressing community norms, these 
accounts undertook efforts to connect to the cultural narratives, stereotypes, and political 
positions of their imagined audiences. Understanding this performative aspect of RU-IRA 
accounts is critical for understanding how the work of information operations not only includes 
activities of disseminating true or false information on social media, but also activities to reflect 
and shape the performances of other (not RU-affiliated) actors in these communities. Taking a 
perspective based on the theory of structuration [21], the impact of these accounts cannot be 
considered in a simple cause and effect type model, but instead should be examined as a 
relationship of mutual shaping or resonance between the affordances of the online environment, 
the social structures and behaviors of the online crowd, and the improvised performances of 
agents that seek to leverage that crowd for political gain.  

Importantly, this activity did not limit itself to a single “side” of the online conversation. 
Instead, it opportunistically infiltrated both the politically left-leaning pro-#BlackLivesMatter 
community and the right-leaning anti-#BlackLivesMatter community. Though the tone of 
content shared varied across different accounts, in general these accounts took part in creating 
and/or amplifying divisive messages from their respective political camps. In some cases (e.g. 
@BleepThePolice), the account names and content shared reflected some of the most highly 
charged and morally questionable content. Together with the high-level dynamics revealed in the 
network graph (Figure 2), this observation suggests that RU-IRA operated-accounts were 
enacting harsh caricatures of political partisans that may have functioned both to pull like-
minded accounts closer and to push accounts from the other “side” even further away. Though 
we cannot quantify the impact of these strategies, our findings do support theories developed in 
the intelligence field that suggest one goal of specifically Russian (dis)information operations is 
to “sow division” within a target society [32, 45]. This study also offers some insight into how 
such an effort works, by leveraging the affordances and social dynamics of online social media. 

6.3 The Challenge of Regulating through Authenticity 

As social media platforms (e.g. Twitter, Facebook) begin to acknowledge the problem of 
information operations and to devote resources and attention towards addressing it [53], one 
repeated refrain has been that these companies do not want to be “arbiters of truth” or seen as 
censoring political content. This is likely because they are wary of removing posts by ideological 
believers of that content. This is important here, because the vast majority of accounts in the 
conversations described in this research—the nearly 22,000 other accounts in our Twitter 
collection—would likely fall into the category of ideological believers (not RU-IRA agents). 

Reluctant to take on the role of deciding what kinds of ideologies are valid and/or appropriate, 
the platforms are therefore faced with a challenge of developing other criteria for determining 
what kinds of activities to promote, allow, dampen, or prevent on their platforms. One recent 



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focus has been on “authenticity” [53]—which could be defined as whether an account is who it 
pretends to be and whether the account believes the content it is sharing and/or amplifying. The 
RU-IRA invested considerable time in developing online personas for their operations, yet these 
accounts do not qualify as authentic by these criteria. So, this developing strategy demonstrates a 
potential way forward that allows the platforms to walk the fine line between criticisms of 
rampant manipulation and concerns about censorship. 

Still, our research suggests that those wishing to deceive are working hard to establish the 
appearance of “authenticity”. To underscore that point, personas featured in this research were 
“authentic” enough for @jack (Twitter’s CEO) and at least one of our researchers to retweet, and 
we assume it will be challenging for platforms to determine authenticity for the vast number of 
active accounts. We do not know how difficult or easy it was for Twitter to identify the RU-IRA 
accounts featured here, but we can assume that developing mechanisms for determining 
authenticity—and even refining the criteria for what authenticity means—represents an 
important and challenging direction for future work.  

6.4 Information Operations and the Challenges Ahead 

Through interactions with and reactions from other users and the connections displayed by 
linking to their own network of websites, the RU-IRA accounts developed unique and individual 
profiles. Discerning between a legitimate social media profile and one constructed by the RU-IRA 
is a complicated—and emotionally fraught—task. Our own experiences of conducting this 
research have taught us that calling out and problematizing accounts as impersonators or 
information operators can be challenging, especially when those accounts align closely with 
one’s own values and worldviews. Despite having a certain level of critical awareness, an 
understanding of the context, knowledge of populist rhetoric, and an “official” list of suspended 
accounts, we found ourselves experiencing doubt when linking some of these accounts with 
pejorative terms like ‘trolling’ and ‘propaganda’. This was especially true when we immersed 
ourselves with RU-IRA data in the ways that most closely resemble how an ordinary social media 
user would encounter their content.  

Crucially, we observed that our own biases made it difficult to problematize certain RU-IRA 
accounts in the left-leaning cluster when we were analyzing their tweets. This highlights how the 
ways in which we make sense of information is significantly impacted by our self-identity and 
the ‘tribes’ [25] we associate with. Since these accounts tried to present themselves as members 
of our ‘tribe’ and speak to our truths (i.e. using information laden with progressive values shared 
by members of our research team), we were sometimes left in a state of doubt and confusion as to 
whether these left-leaning accounts were bad actors at all. We would express doubts concerning 
Twitter’s methodology for identifying these accounts, requesting each other to rerun certain 
analyses, and generally searching for anchors to ground us and give us certainty. At one level, 
this provides another small piece of evidence to suggest that these tactics are effective at what 
many have argued they intend to do—sowing doubt, creating confusion. 

It also raises important questions for researchers and educators: What kinds of emotional and 
critical literacies do we need to cultivate to accurately evaluate credible profiles on social 
networks and effectively challenge information operations? How can we help users look past 
their individual interactions with inauthentic accounts to see the larger patterns of activity 
behind information operations? How can users become more critical of information produced 
through aggressive and reductive messages? While we support efforts by social media companies 
to take responsibility to curb propaganda on their platforms, we also feel that it is important for 
researchers to “intervene” in the sense of helping to call attention to these forms of manipulation 



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and to help the public (and social media companies) understand these phenomena, including how 
and where users are being targeted. CSCW researchers, specifically, can help by furnishing 
conceptual frameworks for better understanding the activities of information operations as 
interactive, and in some ways collaborative efforts that enlist the online crowd (often without 
their knowledge) in their campaigns.  

7 CONCLUSION 

This study examined the online activities of social media accounts affiliated with an organization 
that has been accused of functioning as part of the Russian government’s intelligence and media 
apparatus [61, 62]. We focus on the activities of these accounts—i.e. their information operations—
within #BlackLivesMatter discourse during 2016, during the lead-up to the U.S. presidential 
election. Our research demonstrates how these accounts presented themselves as “authentic” 
voices on both sides of a polarized online discourse, modeling pro- and anti-BlackLivesMatter 
agendas respectively. We also show how these accounts converged to undermine trust in 
information intermediaries like ‘the mainstream media’. This work conceptually sheds light on 
how information operations use fictitious identities to reflect and shape social divisions. We 
conclude by highlighting both the need and the challenges of evaluating authenticity within 
social computing environments. 

ACKNOWLEDGMENTS 
This research is a collaboration between the emCOMP lab and DataLab at the University of 
Washington and was supported by Office of Naval Research Grants N000141712980 and 
N000141812012 as well as National Science Foundation Grant 1749815. We also wish to thank the 
UW SoMe Lab for providing infrastructure support. 

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Received April 2018; revised July 2018; accepted September 2018.