# An Analysis of Twitter Discourse on the War Between Russia and Ukraine

Haris Bin Zia1, Ehsan Ul Haq2, Ignacio Castro1, Pan Hui3, and Gareth Tyson1,3
1Queen Mary University of London, 2Hong Kong University of Science and
Technology,
3Hong Kong University of Science and Technology (GZ)
Corresponding author<EMAIL_ADDRESS>
###### Abstract

On the 21st of February 2022, Russia recognised the Donetsk People’s Republic
and the Luhansk People’s Republic, three days before launching an invasion of
Ukraine. Since then, an active debate has taken place on social media, mixing
organic discussions with coordinated information campaigns. The scale of this
discourse, alongside the role that information warfare has played in the
invasion, make it vital to better understand this ecosystem. We therefore
present a study of pro-Ukrainian _vs._ pro-Russian discourse through the lens
of Twitter. We do so from two perspectives: (i) the content that is shared;
and (ii) the users who participate in the sharing. We first explore the scale
and nature of conversations, including analysis of hashtags, toxicity and
media sharing. We then study the users who drive this, highlighting a
significant presence of new users and bots.

##  1 Introduction

Tensions between Russia and Ukraine have been rising since the 2014 annexation
of the Crimean Peninsula [39]. On 21st February 2022, the conflict escalated
after Russia recognised the Donetsk People’s Republic and the Luhansk People’s
Republic (two Ukrainian breakaway statelets as independent regions). Later on
24th February, the Russian government began a ”special military operation” in
Ukraine, amounting to a full-scale invasion [41]. Anecdotally, this has been
accompanied by a flurry of social media activity both by citizens and
coordinated state actors.

This is not a new phenomena. Increasingly, social networks have played an
important role in conflicts. For example, citizens might use social media
platforms to share their opinions, call for assistance, and report human
rights violations. Whereas, similarly, state actors might misuse such
platforms to spread propaganda and misinformation [16]. This has serious
consequences, and undermines faith in public discourse [43].

Considering the severity of the Russia-Ukraine crisis, we argue it is vital to
gain a better understanding of the ongoing discourse. Key questions include
(i) what type of discussions occur on Twitter and how much are they influenced
by real-world events? (ii) How much, if at all, are conversations around the
conflict becoming toxic? (iii) Do countries withhold content in their
territories during the time of conflict, and does this have political goals?
and (iv) How, if at all, do inauthentic and automated accounts amplify certain
narratives during conflict?

To explore these questions, we perform the first large-scale analysis of
political discourse on Twitter during the Russia-Ukraine war. We focus on
Twitter due to the active role it plays in information warfare [16]. Our
analysis is performed across two major axes. First, we explore the types of
content shared that discuss the war. (§3), before inspecting the users who
share such material (§4).

We find that pro-Ukrainian content is more prevalent on Twitter than pro-
Russian content, and that the online discourse is tightly coupled with the
real-world political events (§3.1). For instance, we see significant spikes in
posting activity around the start of invasion and the UN General Assembly
vote. We also observe differences across regions and languages (§3.2).
Unsurprisingly, Western languages dominate the dataset, with a large volume of
posts from Europe and the US. In-line with common thinking, our results show
that Ukraine gathers support from the global West, whilst users in the sub-
continent and Africa are more inclined towards the Russian narrative. Posting
of URLs is commonplace (largely news media), but interestingly we find regular
hijacking of the narrative by people posting links to things like pornography
and spam (§3.3).

We also inspect the reactions of the two countries to the opposing narratives.
We observe evidence of heightened toxicity within the tweets (§3.4),
particularly for pro-Russian content. 14.7% of the tweets containing pro-
Russian hashtags are toxic compared to just 5.9% of pro-Ukrainian tweets.
However, a more granular view shows that tweets containing the word “Putin“
are more toxic than that of any global leaders. Often this involves pro-
Ukranian users ’hijacking’ pro-Russian hashtags as part of their critique. We
conjecture that this toxicity may result in countries using Twitter’s withheld
feature (which blocks particular users and posts from appearing on a country’s
timelines). Indeed, we find that tweets are withheld from both sides as a
consequence of the conflict (§3.5). Countries in Europe withhold the most
tweets, and the largest fraction of these originate from Russia. For example,
we see common blocking of posts from state-owned media _e.g._ @RTarabic and
@RT_russian. We also discover that a significant surge of posts come from new
users (§4.1), which makes us suspect bots may play an active role in driving
discourse (§4.2). In-line with anecdotal evidence, we find that a large number
of pro-Russian tweets are driven by bots. Surprisingly, we also find highly
automated pro-Ukrainian bots too. We conclude by inspecting the retweet graphs
of users who are active in the debate (§4.3), highlighting that accounts that
use pro-Ukrainian hashtags have clustered more tightly compared to pro-Russian
hashtags.

##  2 Data Collection

(a)

(b)

Figure 1: (a) Number of tweets collected vs. number of tweets posted in each
hour. (b) Number of tweets (per hour) of different types. Note log scale on
y-axis.

Tweet Collection. We use Twitter’s Search API [37] to collect tweets related
to the conflict from February 21, 2022 to March 05, 2022. We do not continue
data collection beyond this point because the Russian state blocked Twitter in
March. We leverage a list of keywords bootstrapped from trending words &
hashtags on Twitter and recent studies around Russian-Ukraine war [29, 28] to
search tweets. These keywords are ‘ukraine’, ‘russia’, ‘putin’, ‘zelensky’,
‘kyiv’, ‘russian’, ‘ukrainian’, ‘keiv’ and translation of these keywords in
Ukrainian & Russian language.

Location Tags. Twitter allows its users to tag their tweets with a location
This contains the geo-coordinates of the location from where the user posted
the tweet. In our dataset, $<1$% of the tweets contain this information. Thus,
following [18, 8], we rely on the location manually specified by users in
their profiles. User specified locations are free-form strings that we map to
a country code using OpenStreetMap Search API [23]. Upon applying this
methodology, we tag 84.43% of tweets with their location.

Toxicity. To analyze the extent of toxic material in our dataset, we label all
tweets containing pro-Russian and pro-Ukrainian hashtags using Google Jigsaw’s
Perspective API [27]. For a given tweet, Perspective returns a score between 0
and 1 for its toxicity (0 $=$ non-toxic). Specifically, we use the API’s
“SEVERE_TOXICITY“ attribute that defines toxicity as “a very hateful,
aggressive, disrespectful comment or otherwise very likely to make a user
leave a discussion or give up on sharing their perspective.“

Data Summary. Figure 1(a) presents the number of tweets collected and the
number of tweets posted per hour. The percentage of collected tweets on
different days varies between 12.8-39.9% of the tweets posted. In total, our
dataset consists of 25,177,195 tweets posted by 5,520,235 users. Not
surprisingly, the majority of the tweets in our dataset are retweets followed
by replies, original and quotes. For context, the temporal distribution of
different tweet types is shown in Figure 1(b).

Ethical Considerations. The data in this work has been collected only using
Twitter API respecting the Twitter’s terms of service. We have exclusively
collected the publicly available data, followed well established ethical
procedures for social data, and obtained an approval from the ethics committee
at the first author’s institution. We have not identified individual users
with the exception of a handful of public accounts whose tweets went viral in
the context of this study. The anonymized version of the data used in this
study is available upon request.

##  3 A Content Perspective

This section investigates the content characteristics of the tweets we
gathered that discuss the war.

###  3.1 Hashtags

(a)

(b)

Figure 2: (a) Top 30 hashtags along with their frequencies. (b) Number of
tweets (per hour) containing pro-Russian vs. pro-Ukrainian hashtags.

We start by inspecting the common hashtags encountered. We argue that these
can provide insight into the nature of ongoing discussions, as well as the
viewpoints of the participants.

Popular Hashtags. Figure 2(a) presents the frequency distribution of the top
30 hashtags. These hashtags are mostly stakeholder names that are directly
involved or somehow affiliated with the war (_e.g._ #ukraine, #russia,
#putin). Some other widely used hashtags express explicit solidarity with
Ukraine (_e.g._ #standwithukraine, #stopputin) while other show support for
Russia (_e.g._ #istandwithputin, #istandwithrussia). Apart from these
hashtags, other highly used hashtags include #anonymous, which refers to the
announcement by the decentralized online hacking group Anonymous that they
would be targeting Russian government websites and infrastructure.
Interestingly, among the top hashtags, we also find a Thai language hashtag
that translate to the word Ukraine.

Figure 2(a) also shows the distribution of hashtags with respect to tweet
types. Retweets amplify almost all hashtags except #stopputin which is most
frequently used in original tweets. This suggests that such tweets are largely
generated by invested actors, but fail to gain wider traction via retweets.
Another contrasting trend is observed in the case of #stoprussia, where
replies surpass original tweets. Such hashtags appear to engender lively
discussions, leading to long threads.

To explore how these hashtags are used, we manually examine the 500 most
frequent hashtags and identify the ones that show clear association with a
certain stance (pro-Russia _vs._ pro-Ukraine). In total, we find 38 pro-
Ukrainian hashtags that occur in 887,388 tweets, and 11 pro-Russian hashtags
that are used in 218,417 tweets. We report these hashtags and their frequency
of occurrence in Appendix A.

Temporal Distribution. Figure 2(b) shows the temporal distribution of tweets
containing pro-Ukrainian vs. pro-Russian hashtags. We observe contrasting
differences in the amplification of hashtags supporting either of the two
sides. Specifically, the online narrative is tightly coupled with real-world
events. The support for Ukraine became visible on the platform when
#StandWithUkraine started trending on 21st of February, _i.e._ after the
Russian president recognised two breakaway regions in Eastern Ukraine as
independent entities. This support was further amplified on and after 24th of
February (_i.e._ the start of the invasion) with hashtags #StandWithUkriane,
#IStandWithUkraine, #StopRussia and #StopPutin. In contrast, the first pair of
pro-Russian hashtags (#Hypocrisy & #AbolishNato) gained momentum between 27th
and 28th of February after NATO condemned Russia’s full-scale invasion of
Ukraine and said that it would deploy more troops to eastern Europe.
Similarly, pro-Russian users used another pair of hashtags _i.e._
#IStandWithPutin & #IStandWithRussia to show solidarity with Russia when the
United Nations General Assembly (UNGA) convened an emergency session to vote
on resolution against Ukraine invasion. Such hashtags shed clear light on
international sentiment.

###  3.2 Language & Location

We conjecture that the above support might vary based on language and
location.

Language. Twitter automatically assigns a BCP 47 language tag [40] to each
tweet. In line with our expectation, tweets in our dataset are predominantly
English, as the majority of our search keywords are in English. Table 1 lists
the top 10 languages. Clearly, Western languages dominate. We also report the
percentage distribution of top 10 languages in pro-Ukrainian and pro-Russian
tweets. English, being the most prevalent language overall, also dominate
these subsets.

Language | Code | # tweets | Distribution (%)
---|---|---|---
original | replies | quotes | retweets | total | pro-Ukrainian | pro-Russian
English | en | 1,500,392 | 2,344,185 | 427,199 | 15,559,215 | 19,830,991 | 83.04 | 95.36
Spanish | es | 94,091 | 122,184 | 31,621 | 909,770 | 1,157,666 | 1.45 | 0.14
French | fr | 77,296 | 75,318 | 17,943 | 631,080 | 801,637 | 1.26 | 0.48
Portuguese | pt | 74,706 | 98,447 | 19,241 | 352,295 | 544,689 | 0.22 | 0.04
German | de | 68,176 | 91,112 | 13,114 | 286,907 | 459,309 | 2.24 | 0.53
Undefined | und | 93,643 | 77,720 | 38,095 | 191,195 | 400,653 | 4.23 | 1.40
Italian | it | 42,369 | 44,953 | 7,436 | 180,595 | 275,353 | 0.37 | 0.23
Turkish | tr | 39,093 | 27,623 | 7,201 | 162,239 | 236,156 | 0.99 | 0.03
Japanese | ja | 20,830 | 4,864 | 2,875 | 176,905 | 205,474 | 0.74 | 0.01
Russian | ru | 24,325 | 37,727 | 2,770 | 135,386 | 200,208 | 0.53 | 0.17
Table 1: Top 10 languages with their codes, number of tweets in the dataset,
and percentage distribution in pro-Ukrainian & pro-Russian tweets.

(a)

(b)

(c)

Figure 3: (a) Location of origin of tweets. (b) Location of origin of tweets
containing pro-Ukrainian and pro-Russian hashtags. (c) Interaction flow (left
to right) for retweets (yellow green), quotes (red) and replies (gold). The
user from location on the left retweet, quote or reply a user from location on
the right.

(a)

(b)

Figure 4: (a) Top 20 URLs in the dataset. (b) Top 20 URL categories in the
dataset.

Locations. Figure 3(a) shows the location origin of tweets (based on their
user location). The majority of tweets originate from Western countries with
an exception of India, which ranks 3rd. Only a limited number of tweets in our
dataset originate from Russia. This is naturally impacted by the censorship of
Twitter in Russia. To better understand the types of regional support that
exist, we inspect the location of tweets containing pro-Ukrainian vs. pro-
Russian hashtags. Figure 3(b) shows the top 5 countries along with the number
of tweets that originate from them for both subsets. This shows that the
location of tweets supporting Ukraine is significantly different to that
support Russia. Our results reveal that support for Ukraine mostly comes from
the users in the global West whereas users from sub-continent and African
countries are more inclined towards Russian narrative. We also briefly examine
locations in scenarios where a user re-shares (retweets, quotes) or replies to
other tweets. Figure 3(c) shows the interaction flow for retweets, quotes and
replies based on location. The majority of time users interact with others in
the same country. Interestingly, users from United States interact much more
often with tweets originating from Ukraine than that from Russia.

###  3.3 URL Sharing

We next turn to the shared URLs to explore what type of content that is posted
related to the war. We observe nearly 2.4 million unique URLs (80,815 unique
base URLs), which are shared a total of 15 million times. Figure 4(a) lists
the top 20 domains posted. Most of the widely used domains are either
mainstream news websites (_e.g._ Reuters, New York Times) or social media
platforms (_e.g._ YouTube, Facebook). We also find t.me URLs among the top 10
most posted domains. These URLs correspond to the popular messaging platform,
Telegram, which became the go-to messaging app for Ukrainian due to its unique
features such as ‘secret chat’ [3].

To better understand the nature of these tweets, we classify the domains
shared using FortiGuard [20]. We identify 86 different domain categories with
23% domains belonging to ‘News and Media‘. Figure 4(b) presents the top 20 URL
categories in the dataset. Interestingly, we also find a small number of
‘Pornography‘ (327), ‘Phishing‘ (202) and ‘Spam‘ (53) URLs. Such users appear
to be exploiting the viral popularity of Ukrainian posts to promote these
links.

###  3.4 Toxicity Analysis

Polarization and toxicity in political discourse is common on Twitter [32].
Figure 5(a) shows the Cumulative Distribution Function (CDF) of the
Perspective toxicity scores for the tweets containing pro-Russian and pro-
Ukrainian hashtags. In the literature, 0.5 is the most common choice to
threshold the perspective scores [5, 31, 25], however, higher values such as
0.8 are also used [1]. Here, we use 0.5 as a threshold and consider a tweet to
be toxic if its toxicity score is greater than 0.5 (and vice versa).

Given this threshold, we find that 14.7% of the tweets containing pro-Russian
hashtags are toxic compared to just 5.9% of pro-Ukrainian tweets. The toxic
tweets with pro-Russian hashtags are mostly anti-US/NATO and cite the
hypocrisy of the West over conflicts in Palestine, Syria and Iraq (using swear
words). In contrast, the toxicity in the pro-Ukrainian tweets is mostly
directed towards Russian president Putin and former US president Trump. In
fact, among the tweets mentioning important political figures the tweets
mentioning Trump has the second highest toxicity percentage (Figure 5(b)),
even though the invasion took place after Trump left office. Closer inspection
suggests that Trump attracted this backlash after calling Putin a
genius.111https://edition.cnn.com/2022/03/03/politics/trump-putin-russia-
ukraine-graham/index.html We argue that such insights can be vital for
understanding the wider public reaction to political figures and policies.

(a)

(b)

Figure 5: (a) CDF of the toxicity scores for the tweets containing pro-Russian
and pro-Ukrainian hashtags. (b) CDF of the toxicity scores for the tweets
mentioning Zelenskyy, Biden, Trump or Putin.

###  3.5 Withheld Tweets

Figure 6: Fraction of tweets withheld in a country on y-axis that originate
from a country on x-axis.

(a)

(b)

Figure 7: (a) Number of tweets (per hour) containing #IStandWithPutin,
#IStandWithRussia and #IStandWithUkraine hashtags. (b) Number of users joined
(daily) since January 01, 2022 till March 10, 2022 in pro-Russian and pro-
Ukrainian subsets.

The above level of toxicity leads us to conjecture that relevant tweets may be
withheld in certain jurisdictions. Note, this is where the state authority
requires Twitter to prevent such tweets from being seen by users in that
country.222See https://help.twitter.com/en/rules-and-policies/tweet-withheld-
by-country

In total, 56,512 tweets (0.22% of the tweets in our dataset) are withheld
across 33 different countries. These tweets are collectively posted by 25,733
users, out of which 113 are verified and 448 have their profile withheld in at
least one country. Figure 6 shows the fraction of tweets withheld in a country
(y-axis) that originate from each country (x-axis) as a heatmap. Here, we only
plot countries that withhold at least 100 tweets or at least 100 tweets
originating from them has been withheld. Countries in the European Union and
United Kingdom withhold the most tweets and the largest fraction of these
tweets originate from Russia. Some of the prominent Russian accounts whose
tweets are withheld are state-owned media _e.g._ @RTarabic and @RT_russian.
These accounts primarily share news articles about the Russia-Ukraine war in
their tweets.333Note, evaluating the authenticity of these news articles is
beyond the scope of this work Unsurprisingly, 10.6% of tweets withheld in
Russia originate from Ukraine (note, in March, Russia blocked all access to
Twitter [13]). The tweets withheld in Russia are from accounts that are very
vocal about Russian Invasion of Ukraine, _e.g._ @ChechenCenter - an
independent media from Chechen Republic of Ichkeria. Since Twitter does not
provide the date a tweet is withheld, it is hard to say how many tweets are
withheld as a consequence of Russia-Ukraine conflict. However, it is evident
that not all of the tweets are withheld in the aftermath of Russia-Ukraine
war, _e.g._ we observe a large fraction of tweets withheld in India that
originate from Pakistan — this is simply because of the usual political
tensions between the two countries [10].

##  4 A User Perspective

The previous section has shown that there is a wide range of both pro-Russian
and pro-Ukranian discourse, with a unusually high prevalence of toxic text. We
next proceed to explore the users sharing this content. To understand, the
partisan nature of the users, we extract all users who have used the pro-
Russian #IStandWithPutin and #IStandWithRussia hashtags; then all the users
who have posted on the #IStandWithUkraine hashtag. To expand this dataset, we
further gather all the tweets posted between February 21, 2022 and March 10,
2022 containing these three hashtags. Figure 7(a) plots the count of tweets
for each hashtag. In total, we collect 260,138 tweets with #IStandWithPutin,
149,106 with #IStandWithRussia and 638,217 with #IStandWithUkraine.

###  4.1 New Users

(a)

(b)

(c)

Figure 8: (a) Bot score distribution for tweets from both pro-Russian and pro-
Ukrainian subsets. (b) Percentage of tweets posted by likely bots using 0.5 as
a threshold. (c) Percentage of tweets posted by likely bots using 0.7 as a
threshold.

We first inspect the number of new users who contribute to online discussions.
Tweets in the pro-Russian subset (_i.e._ containing #IStandWithPutin &
#IStandWithRussia) are posted by 120,370 unique users, whereas the ones in
pro-Ukrainian subset (_i.e._ containing #IStandWithUkraine) are posted by
298,867 unique users. We conjecture that new users are more likely to be
inauthentic as they may be put in place just to coordinate campaigns and
amplify narratives.

To explore this, we look at the creation dates of users in the pro-subsets.
Figure 7(b) shows the number of users who joined (daily) since January 01,
2022 till March 10, 2022 in pro-Russian and pro-Ukrainian subsets. The pro-
Russian subset contains 9,008 users who joined between January 01, 2022 and
March 10, 2022. These users collectively posted 30,343 tweets. In comparison,
pro-Ukrainian subset contains 11,854 such users who posted a total of 25,284
tweets.

Interestingly, we observe a large spike in the number of new users on and
after February 24, 2022 (_i.e._ in the early days of invasion) followed by a
decreasing trend after a week, in both subsets. Nearly 132% more users joined
in the week after the invasion compared to the week before it, collectively in
both subsets. Curiously, opposed to one large peak in the pro-Ukrainian
subset, we observe two dramatic peaks in the pro-Russian users: one at the
beginning of the invasion, and the other on the day of UN General Assembly
session. This influx could be driven by genuine social media users interested
in the public discourse, just as the subsequent decreasing trend could be due
to a decline in public interest or Russia’s ban on Twitter (March 04, 2022
[13]). However, we conjecture that these patterns could also be driven by an
influx of coordinated accounts (_e.g._ bots) driven by state actors.

###  4.2 Presence of Bots

To see whether these new users are bot-like, we pass all the users created on
or after January 01, 2022 through Botometer [9].444Botometer is a supervised
machine learning classifier that distinguishes bot-like from human-like
accounts based on their 1000+ features _e.g._ length of screen name, profile
picture and background, age of account etc. For each user account, Botometer
provides a “bot score“ between 0 and 1: a score close to 1 means the account
is highly automated, while a score near 0 means a human is likely handling the
account.

We plot the bot score distribution for tweets posted by new users from both
pro-Russian and pro-Ukrainian subsets in Figure 8(a). Since we are interested
in the bot activity level of pro-hashtags, we use tweets (as opposed to
accounts) as the units of analysis. In both the subsets, the distribution has
a bimodal pattern. The spikes appear to be driven by accounts tweeting the
same hashtag repeatedly. For example, the spike at 0.84 in the pro-Ukrainian
subset comes from a bot account that tweets “F**k you, Putin¡‘ every minute in
various languages.

While this particular parody bot account is honest about itself, the majority
of the bots are not. Thus, to calculate the proportion of tweets from likely
bots in both pro-subsets, we consider accounts with scores higher than a
threshold as likely bots. In the literature, 0.5 is the most common choice of
threshold [33, 4, 38], however, higher values, such as 0.7 [12] and 0.8 [6],
are also used. We use both 0.5 and 0.7 as thresholds and show the results in
Figure 8(b) and 8(c) respectively.

These results confirm a significant presence of bots on _both_ sides, albeit
with pro-Russian hashtag posts more in number. We apply two-proportions
$z$-tests to estimate the significance level of the differences. When using
0.5 as the threshold, the percentage of tweets from likely bots that mention
pro-Russian hashtags is significantly higher than those that use pro-Ukrainian
hashtags ($p<0.01$). Interestingly, the results reverse when we apply 0.7
threshold _i.e._ the percentage of tweets from likely bots in the pro-Russian
subset becomes significantly lower than that in pro-Ukrainian subset
($p<0.01$). If we revisit Figure 8(a), we see that although the bot score
distribution of pro-Russian hashtags has more mass in the (0.5, 1] region than
the distribution of pro-Ukrainian hashtags, the mass tends to concentrate
around 0.6. In contrast, the distribution of pro-Ukrainian hashtags has more
mass around 0.85. This nuanced difference causes the contradictory results
when using different thresholds. Put simply, new users that tweeted in support
of Russia show more bot-like activity. Yet the pro-Ukranian bots exhibit more
obvious traits. This stands in contrast to anecdotal popular opinion and we
conjecture that this may be driven by greater sophistication among the pro-
Russian bots.

###  4.3 User Interactions

We conjecture that the above bots may drive certain patterns in the retweet
network. Thus, to investigate any attempts at platform manipulation [36] in
building or amplifying a certain narrative, we next look into the most
influential tweets in both pro-subsets.

Community Exploration. Figure 9 presents the interaction graph of users in
both pro-Russian (Figure 9(a)) and pro-Ukrainian (Figure 9(b)) subsets. Here,
each node represents a user and each edge represents a retweet directed from a
retweeting to retweeted user. The node and label sizes are directly
proportional to the number of retweets _i.e._ the higher the number of
retweets a user receives the bigger is its node and label. Each colour
represent a different community _i.e._ the users that tend to interact with
each other.

(a)

(b)

Figure 9: Interaction graph of users in both (a) pro-Russian and (b) pro-
Ukrainian subsets. Each node represents a user and each edge represents a
retweet directed from a retweeting to retweeted user.

Pro-Russian Patterns. In case of the pro-Russian subset, we observe two
distinct groups. The first group (left in Figure 9(a)) consists of many dense
communities that use the pro-Russian hashtags to support the Russian narrative
whereas the second cluster (right) consists of a single community that used
the pro-Russian hashtags, but to condemn the Russian cause. This highlights
the challenges of using the hashtags to assign stance to the users — many
people include supportive hashtags from the opposing side as part of their
critique. The most retweeted account from the first cluster is @Sunny_Rajput —
a relatively new account (joining in September 2021) of Indian origin but
claims itself to be from United States (396 followers and follows 1,301). The
tweet posted by this account received 5,171 retweets. Other popular tweets in
this cluster are from @slimDestro (joined: March 2011, from: India, followers:
102, follows: 5, retweets received: 3,811), @sachin012yadav (joined: December
2015, from: India, followers: 74, follows: 90, retweets received: 3,630) and
@anmology (joined: October 2018, from: India, followers: 145, follows: 50,
retweets received: 3,362). Interestingly, top 10 most retweeted tweets in this
cluster received suspiciously large number of retweets than the number of
followers of accounts who posted them, raising questions over the virality of
these tweets. The tweets posted by these accounts centre around Western
hypocrisy, double standards and support for Putin.

The right cluster in Figure 9(a) is rather different, with the most viral
tweet coming from @NickKnudsenUS. This user joined in November 2016 from
United States, has 229,517 followers, follows 19,413 and received 4,064
retweets. In contrast to the pro-Russian tweets in the left cluster, this one
is actually using the pro-Russian hashtag #IStandWithPutin to condemn the war,
calling Putin’s supporters fascists. This again highlights the challenge of
deriving stance from the use of hashtags alone.

Pro-Ukranian Patterns. The interaction graph of users using pro-Ukrainian
hashtags is rather different, with fewer clear communities of users emerging.
This suggests that pro-Ukranian prominent voices are consolidated around a
much small number of key players. Users here show solidarity with Ukraine,
raise voices for aid, express anger towards Trump, and call Putin a war
criminal. The most retweeted tweet here is from @nseskuria, who posted a photo
of the Mayor of Kyiv in the war zone. This account joined in October 2014,
originating from the United Kingdom, has 8,211 followers, follows 2,002 and
received 23,976 retweets. Manual inspection confirms this is not a bot. Other
popular users in the pro-Ukrainian subset include @i9heart (joined: December
2021, from: N/A, followers: 232, follows: 210, retweets received: 15,458),
@johnpavlovitz (joined: February 2012, from: United States, followers:
382,191, follows: 327,69, retweets received: 11,224) and @MuellerSheWrote
(joined: November 2017, from: United States, followers: 241,619, follows:
8,378, retweets received: 6,634). We argue that these patterns can support
further analysis to understand the coordinated spread of posts.

##  5 Related Work

Studies of Real-World Events. There have been a number of prior studies
looking at real-world events through the lens of social media [21]. Social
media has been studied as an effective tool for factual reporting and
situational awareness [2]. Social media has also been used to study opinion
engineering [34], political polarization [15, 21], and propaganda campaigns
[19] in social events.

Social Media & War. The Russia-Ukraine war has already been the focus of
several studies [14, 17, 7]. These have had a particular emphasis on
information quality [29]. Similar to our own work, both Ukrainian and Russian
supporters have been part of information warfare across various social media
and online platforms [8, 16]. One of our key findings is that countries are
withholding tweets. A recent comparison of Twitter and Facebook found that
most accounts involved in propaganda are politically right-leaning. However,
content moderation on such platforms could only remove up to 15% of such
content [29]. Our results also show that many new accounts participate in
discussions related to the war. Pierri et al. studied banned accounts during
the conflict. They found that such accounts’ creation and suspension dates are
clustered around temporal epochs [28]. They also found that such accounts
primarily interact with normal users through replies and mentions.

Besides censorship, selective media reporting has also been observed widely.
Conflict coverage from Russian state-affiliated media has been found to differ
from independent media; reducing conflict situational awareness to the Russian
population and leaving them more vulnerable to disinformation [26]. The race
to share news quickly also compromises the quality. For example, fake videos
from platforms like TikTok have been shared without fact-checking from several
media outlets [35]. Another study has shown that users who report fake news
exhibit “motivated reasoning”. This study found that Ukrainian users are more
like to endorse fake news regarding Russia as compared to fake news for the
European Union [24].

Other systematic ways to spread misinformation and propaganda are also
identified during the war. One study found that most of pro-Russia campaigns
were geographically distributed and about 20% of content came from the bots
created during the earlier days of the crisis [11]. In addition, the use of
social issues and polarization, such as Gun Control in the US, is also
highlighted as one of the prominent way to spread the online disinformation
during the war [42].

Others have measured public sentiment related to the war via social media data
[22]. In-line with our findings, they show that support varies based on
region, with users from Western countries being most supportive of the
sanctions on Russia. Interestingly, Twitter sentiment also correlated with
Forex rates and crude oil prices [30].

Novelty. Our work supplements previous works on Twitter discourse during the
conflict. We particularly highlight the posting across partisan hashtags,
differences among regions, and censorship by different countries. In addition,
we highlight the role of automated accounts to amplify certain narratives.

##  6 Conclusion

This paper has presented a study of Twitter discourse surrounding the Russian
invasion of Ukraine. We wish to highlight a set of key findings. We found that
pro-Ukrainian content is more prevalent on Twitter than pro-Russian content,
and that the online discourse is tightly coupled with the real-world political
events (§3.1). We observed differences across regions and languages (§3.2),
with, Western narratives dominating the dataset. Whereas Ukraine gathers
support from the global West, users in the sub-continent and Africa are more
inclined towards the Russian narrative. Toxicity among these discussions is
commonplace, particularly for pro-Russian hashtags (§3.4). For example, 14.7%
of the tweets containing pro-Russian hashtags are toxic compared to just 5.9%
of pro-Ukrainian tweets. This leads to tweets being withheld in certain
countries (§3.5). We found that countries in Europe withhold the most tweets,
and the largest fraction of these tweets originate from Russia. A significant
surge of posts come from new users (§4.1) and in-line with anecdotal evidence,
we find that a large number of pro-Russian tweets are driven by bots (§4.2).

We emphasise that this work constitutes just the first step in exploring the
online discourse surrounding the war. We wish to highlight two key limitations
that we aim to address in our future work. First, our dataset is naturally
biased towards Western perspectives, particularly as Russia blocked Twitter in
March 2022. To address this, we plan to perform similar analysis on more pro-
Russian social networks such as VK. Second, we found significant noise when
using hashtags to assign stance to individual tweets. For instance, many pro-
Russian hashtags were included in pro-Ukrainian tweets (as part of a
critique). Consequently, the above analysis should be interpreted with this
in-mind. To address this, we hope to build more robust classifiers that can
label tweets as pro- Ukraine or Russian, without exclusive reliance on
hashtags. We argue that this is vital as social media will increasingly become
an arena for real-world conflicts.

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##  Appendix A Pro-Ukrainian _vs._ Pro-Russian Hashtags

Pro-Ukraine: #StandWithUkraine (333,999), #StopPutin (130,300), #StopRussia
(96,761), #StandWithUkriane (63,023), #IStandWithUkraine (54,103),
#SafeAirLiftUkraine (49,168), #StopPutinNow (34,985), #StopRussianAggression
(34,178), #PutinIsAWarCriminal (29,502), #SlavaUkraini (27,210), #PutinHitler
(26,153), #PrayForUkraine (23,409), #FckPutin (19,504), #WeStandWithUkraine
(18,388), #StayWithUkraine (13,772), #StandingWithUkraine (13,340),
#HelpUkraine (11,455), #DefeatPutin (11,429), #IStandWithUkriane (11,304),
#SaveUkraine (10,209), #SanctionRussiaNow (9,067), #RussiaGoHome (8,623),
#BanRussiaFromSwift (7,485), #PrayingForUkraine (7,310), #PutinWarCrimes
(6,797), #UnitedWithUkraine (6,697), #FuckPutin (6,595), #NoWarInUkraine
(5,158), #SolidarityWithUkraine (5,008), #GloryToUkraine (4,377),
#BoycottRussia (4,292), #StopRussianAgression (3,976), #StandForUkraine
(3,748), #WeAreAllUkrainians (3,659), #FckPtn (3,319), #SendNatoToUkraine
(3,275), #PeaceForUkraine (2,421), #StandWithUkraineNow (2,213) Pro-Russia:
#IStandWithPutin (134,393), #IStandWithRussia (67,955), #Hypocrisy (18,610),
#AbolishNato (14,404), #RacistEU (12,276), #StandWithRussia (9,451),
#DoubleStandards (7,883), #ISupportRussia (5,929), #NaziUkraine (4,467),
#StandWithPutin (3,228), #SupportRussia (2,254)