1 Introduction
1.1 Contributions
-
We analyze such a dataset, uncovering instances of coordinated behavior previously unexplored in literature. Our results highlight networks of foreign influence that exhibit signs of activism and social movements (e.g., Hong Kong and Nigeria protests), along with conspiracy theorists in the online debate. Moreover, we identify right-wing conspiracy communities as particularly detrimental to the discourse.
-
We apply a state-of-the-art technique to detect coordinated behaviors [9], also extending it by introducing a novel indicator of coordination that simultaneously considers both the level of coordination and the size of the community. Our results demonstrate the usefulness of the indicator towards identifying harmful communities.
-
We characterize each coordinated community along multiple dimensions, going beyond existing works that are mainly focused on inauthenticity. Our nuanced characterization contributes to identifying a wide spectrum of diverse coordinated communities, spanning from modest grassroots activists to riots, and culminating in highly detrimental instances of inauthentic behavior.
1.2 Significance
1.3 Roadmap
2 Related work
2.1 The 2020 US election Twitter debate
2.2 Detection and characterization of coordinated behavior
3 Dataset
Hashtag | Lean | Users | Tweets |
---|---|---|---|
#usa2020elections | N | 3 | 4 |
#usa2020 | N | 988 | 4263 |
#Election2020 | N | 86,305 | 2,560,391 |
#ElectionDay | N | 78,100 | 584,095 |
#Debates2020 | N | 80,267 | 703,307 |
#Vote | N | 85,642 | 2,694,442 |
#VoteEarly | N | 52,497 | 349,301 |
#Ivoted | N | 24,189 | 45,957 |
#JoeBiden | N | 81,193 | 743,605 |
#biden | N | 84,480 | 1,106,452 |
#DonaldTrump | N | 41,513 | 118,199 |
#trump | N | 86,245 | 1,976,369 |
#VoteBlue | D | 27,007 | 153,856 |
#VoteBlueToSaveAmerica | D | 23,464 | 157,107 |
#Biden2020 | D | 48,163 | 141,351 |
#BidenHarris2020 | D | 71,302 | 979,039 |
#joebiden2020 | D | 13,797 | 19,789 |
#NeverTrump | D | 21,534 | 32,740 |
#WakeUpAmerica | D | 34,900 | 122,378 |
#VoteRedToSaveAmerica | R | 43,324 | 380,114 |
#VoteRed | R | 40,748 | 245,643 |
#trump2020 | R | 73,901 | 3,032,268 |
#trumppence2020 | R | 58,679 | 512,048 |
#donaldtrump2020 | R | 2891 | 3808 |
#MAGA | R | 85,216 | 4,863,113 |
#KAG | R | 65,332 | 859,569 |
#NeverBiden | R | 21,293 | 44,045 |
Mentions | Lean | Users | Tweets |
---|---|---|---|
@JoeBiden | D | 87,101 | 12,499,125 |
@DrBiden | D | 57,442 | 288,255 |
@KamalaHarris | D | 86,100 | 2,971,072 |
@SenKamalaHarris | D | 61,019 | 260,468 |
@TheDemocrats | D | 67,105 | 324,688 |
@POTUS | R | 86,482 | 3,670,176 |
@realDonaldTrump | R | 87,115 | 43,131,119 |
@Mike_Pence | R | 81,164 | 1,022,082 |
@VP | R | 82,898 | 853,809 |
@MELANIATRUMP | R | 24,366 | 45,442 |
@FLOTUS | R | 83,908 | 1,260,890 |
@GOP | R | 85,669 | 1,727,397 |
total | – | 87,410 | 71,506,397 |
4 Methods
4.1 Detection
4.1.1 Selecting the starting set of users
4.1.2 Selecting the similarity measure
4.1.3 Building the user similarity network
4.1.4 Filtering the user similarity network
4.1.5 Detecting coordinated communities
4.1.6 Characterizing coordinated communities
4.2 Characterization
4.2.1 Coordination
4.2.2 Automation
4.2.3 Suspensions
4.2.4 Political partisanship
4.2.5 Nonfactuality
5 Results
REP
) features a mix of highly coordinated users (dark-colored) surrounded by those with weaker coordination ties (light-colored). Other communities, such as DEM
, PCO
, IRN
, and QCO
, predominantly display weak coordination. In contrast, the BFR
community exhibits a strong and relatively uniform coordination among its members, highlighted by the prevalence of dark-colored nodes. In the following we shed light on the intent, activity, and potential harmfulness of each coordinated community with a threefold analysis. To provide context for the detected coordinated communities, we first analyze the main narratives discussed by members of each community. Then, we further characterize each community from different angles, including: their degree of coordination, their use of automation, their political partisanship, the degree of factuality of the content they shared, and platform suspensions of coordinated accounts. Lastly, we simultaneously examine all these aspects, providing rich insights into the dynamics of coordinated communities.5.1 Narratives
REP
: Republicans (4522 users). The largest coordinated community discussed hashtags supportive of the Republican candidate and former President Donald Trump, such as trump2020, americafirst, and maga. Despite some engagement with extremist and controversial hashtags, the majority of their activity leaned towards moderate conservatism.CRE
: Conspirative Republicans (1553 users). This community also supported the Republican party. However, differently from REP
, its members were predominantly interested in multiple conspiracy theories, as shown in Fig. 3 by the presence of hashtags such as qanon2018 and qanon2020.
DEM
: Democrats (1039 users). Representing Biden and Democratic supporters, this community discussed topics aligned with Democratic ideals, encouraging early voting and a proactive attitude in the electoral process.IRN
: Iranians (96 users). Narratives within this coordinated community are centered around the Restart movement, with hashtags such as restart_opposition and miga (i.e., Make Iran Great Again). Restart was an Iranian political opposition movement, in support of Donald Trump due to his stance against the Iranian regime. By participating in the online electoral debate, Restart aimed to gain visibility and influence within the US political discourse.8PCO
: Pandemic conspiracies (84 users). Centered around a far-right news media platform that spread controversial narratives, this community engaged in various debated topics, ranging from conspiracy theories related to COVID-19 to political narratives critical of the Chinese government.BFR
: Biafrans (69 users). A dense and strongly-coordinated community advocating for Biafra’s independence from Nigeria. Its members aimed to exploit Trump’s presidency and the upcoming election to attract international support. They promoted their separatist goals and sought broader recognition for their movement.9FRA
: French (49 users). This small coordinated group is mostly composed of French accounts supporting Trump and the election fraud narrative. This community also sought to influence domestic French politics, including the endorsement of far-right presidential candidates [58].ACH
: Anti China (31 users). A small community of Hong Kong protesters mobilized against the Chinese regime. They were showing support for Donald Trump while criticizing Joe Biden and the Democrats for their alleged complicity with authoritarian regimes.11-
Moderate parties: this group includes coordinated communities representing the two major political parties involved in the election (i.e.,
REP
andDEM
). Users from the communities in this group engaged in political discourse with moderate tones. -
Conspiratorial communities: this group includes coordinated communities who supported various conspiracy theories, including those related to QAnon (i.e.,
CRE
,QCO
), election fraud claims (i.e.,EFC
), and the pandemic (i.e.,PCO
). The activity of these types of communities on social platforms typically contributes to the spread of misinformation, shaping false narratives and potentially influencing public opinion. -
Foreign influence networks: coordinated communities in this group have ties to foreign politics (i.e.,
IRN
,BFR
,FRA
,ACH
). Their involvement in the debate was either aimed at undermining public trust and tampering with the democratic process, or to draw international attention to local affairs. Similar coordinated initiatives were observed for the US 2018 midterm elections [62]. Figure 4 shows the predominant language used by the members of each coordinated community. For the communities in this group, the analysis indicates a user base speaking both the native language and English. This hints at strategic involvement with geopolitical agendas within the US 2020 political debate.×
5.2 Multifaceted characterization
5.2.1 Coordination
DEM
and REP
communities representing the main moderate parties, show a downward trend in size at increasing levels of coordination. This suggests a more dispersed and possibly grassroots level of engagement, where individual actors engage in political discourse without a centralized or strongly coordinated strategy. The presence of a small core of strongly coordinated users might drive the agenda of the group, while the broader base reflects a more organic, diverse political conversation. For communities characterized by conspiracy theories we observe diverse patterns of coordination. For instance, CRE
shows a higher level of sustained coordination, as implied by the plateau in its curve. This reflects a strong, cohesive group possibly employing centralized strategies to spread their narratives. In contrast, QCO
lacks a pronounced plateau, suggesting the presence of loosely connected conspiracy theorists. The foreign influence networks, particularly BFR
and ACH
, stand out with their plateaued coordination, reflecting a consistent and stable level of high coordination levels. These communities might employ a strategic organized approach, with possibly centralized coordination to effectively influence or engage with the broader political conversation.
5.2.2 Automation
CRE
community exhibits a strong user presence in Quadrant A, indicating high levels of both automation and coordination. This suggests a reliance on automated accounts to disseminate conspiratorial narratives, potentially to bolster their visibility and influence. For conspiracy communities such as QCO
and EFC
, which do not prominently feature in Quadrant A, the relationship between automation and coordination is more nuanced. These groups may not heavily rely on automated accounts for their activities, suggesting a different, more organic, kind of engagement strategy within the conspiracy theory domain. Overall, these results suggests the lack of a one-size-fits-all relationship between automation and coordination within communities of conspiracy users. Finally, foreign influence network accounts, represented by communities like BFR
and ACH
, are also heavily clustered in Quadrant A, indicating high automation alongside strong coordination. These communities likely involve automated accounts to support their agendas, whether for geopolitical influence or to amplify their messages. In summary, while moderate parties tend toward more organic methods, conspiracy and foreign influence networks appear to strategically leverage more automation to magnify their impact and shape the discourse, albeit with interesting differences.
5.2.3 Suspensions
CRE
and QCO
, often show higher suspension rates, which may correlate with the controversial nature of the content they share. Their higher suspension rates might be a result of systematic platform policy violations, including the spread of false narratives or other forms of disruptive behavior. Instead, foreign influence networks like BFR
, ACH
, and IRN
present diverse suspension rates. Some communities, such as BFR
, maintain a high level of coordination with very few suspensions, indicating an organic or a more strategic approach that carefully avoids crossing platform rules. Others, like ACH
, experience more suspensions, perhaps due to more aggressive tactics or content. These differences may reflect the diverse objectives and strategies within influence networks, from overt political activism to more covert influence operations. Alternatively, they might indicate the limited effectiveness of Twitter at moderating a subset of misbehaviors. Finally, we compare the levels of automation with suspension rates. Figure 8a shows that communities like CRE
and BFR
show high automation scores. Yet, as depicted in Fig. 8b, Twitter’s enforces suspensions towards user of CRE
, but not towards BFR
. In contrast, the QCO
community, while showing lower automation, has a high rate of user suspensions, suggesting that factors beyond automation contribute to Twitter’s suspension decisions. These findings suggest complex, non-linear relationships between automation and suspensions: high automation doesn’t always result in suspensions, and vice versa.
5.2.4 Political partisanship
REP
community exhibits a right-leaning bias, albeit slightly more centrist and moderate compared to the other right-leaning communities. Instead, the DEM
community displays a left-leaning bias with lower coordination values. Conspiracy-oriented communities show a right-leaning bias. The CRE
community, in particular, shows a high coordination value, suggesting a coordinated effort to share a strong partisan viewpoint. Lastly, foreign influence networks show varying degrees of bias. In particular, the BFR
community presents an interesting combination of a politically-centered bias and high coordination. This possibly indicates that while their political messaging may be more moderate or diverse, their approach to spreading these messages is highly organized and possibly strategic, aiming to influence or sway public opinion in a focused manner. In summary, the prevalence of far-right ideologies identified through our community detection criteria aligns with observed political polarization during the election, indicating more strategic utilization of social platforms by conservative groups [60, 61].
5.2.5 Nonfactuality
DEM
community shows a commitment to higher factuality and lower levels of coordination. In contrast, the REP
community shows lower levels of credibility. Conversely, conspiracy-oriented communities generally display low degrees of factuality. Indeed, conspiracy narratives frequently exhibit lower levels of factual reporting and tend to prioritize the reinforcement of ideological beliefs. Remarkably, the CRE
community emerges as the least factual, indicating a high propensity for deceitful behaviors. Finally, foreign influence networks show diverse balance between factual and non-factual content as a means of legitimizing their cause while promoting their strategic objectives. The use of factuality may be instrumental in gathering support and credibility, especially when reaching out beyond their immediate community to influence broader discussions. In summary, each category utilizes a distinct mix of factuality and coordination in their online behavior, reflecting their unique goals and strategies.
5.3 Composite characterization
PCO
feature massive account suspensions. Out of all the coordinated communities that we investigated, CRE
emerges as the most harmful one. Finally, foreign influence networks show overall similar radar charts to those of the conspiratorial communities. Indeed, all foreign influence networks feature high nonfactuality, partisanship, and automation. However, contrarily to conspiratorial communities, all feature relatively low levels of suspensions. This last finding might indicate a reduced effectiveness of Twitter – or a reduced interest – in detecting and moderating these specific types of coordinated groups. Overall, our nuanced results highlight the challenges of distinguish genuine, organic activism from orchestrated influence campaigns. In summary, each community’s radar chart serves as a visual fingerprint of the multifaceted characteristics of that community, combining indicators across multiple dimensions. The analysis of the size and shape of these fingerprints contributes to the identification of genuine grassroots movements and to the understanding of harmful and inauthentic coordinated behavior, thus contributing to the ongoing efforts aimed at safeguarding the health and integrity of the online political discourse.6 Discussion
6.1 The heterogeneous landscape of online coordination
6.2 Offline consequences of online coordination
CRE
), could set the stage for real-world turmoil, similar to what led to the Capitol Hill insurrection [38]. In contrast, communities with high coordination but low partisanship and nonfactuality may reflect organic activism that ignites or reinforces public demonstrations and societal change, reminiscent of the pro-democracy protests in Hong Kong [63‐65] and similar movements in Nigeria [66‐68]. Similarly, a community characterized by mild political opposition and low levels of harmfulness (e.g., IRN
) could hint at hidden discontent that may eventually lead to real actions of dissent [69, 70]. The previous examples highlight the importance of studying online coordination, which holds the potential to influence, and at times also anticipate, significant real-world events ranging from political protests and social movements to market fluctuations and public health crises. Therefore, unraveling the dynamics of online coordination – particularly via nuanced and multifaceted analyses – allows gaining insights that enhance the comprehension of contemporary societal trends, both from a descriptive and possibly even from a predictive standpoint.6.3 Securing the integrity of online political discourse
6.4 Limitations
7 Conclusions
Acknowledgements
FoReLab
projects (Departments of Excellence).