Twitter Metrical Data Analysis Using R: Twiplomacy in the

Outbreak of the War in Ukraine

Dimitrios Vagianos

and Thomas Papatsas

Department of International and European Studies, University of Macedonia, Egnatia 156, Thessaloniki, Greece

Keywords: The Russian-Ukrainian War, Social Networking Analysis, Data Analysis, Content Analysis, Interactivity

Networks.

Abstract: It is common practice for Social Media to be used to inform and sway public opinion during contemporary

conflicts. This study focuses on how Twitter (now known as X) was used with regards to the Russian-

Ukrainian war during the first three months following Russia's invasion of Ukraine on February 24, 2022.

Thirty accounts in total—fifteen from each opposing side—were used to mine the data. The information

released by these accounts throughout this monitoring period, along with the frequency of their postings, were

collected and investigated in order to highlight the diverse approaches in this kind of cyberspace confrontation.

In order to emphasize the key components of each party's strategy and its efficacy, the interactivity networks

of the accounts under discussion were constructed and visually analysed. Overall, this research attempt

exploits a combination of effective data analysis approaches including word frequencies’ investigation and

interactivity networks analysis based on the modularity community detection algorithm. By exclusively using

Open Source software, the results visually highlight the degree of coordination and intensity of Twitter use

of the Ukrainian side, a fact that is in full accordance with the comparatively more successful induced

influence Ukraine achieved during this time frame, as this has been reported by the media generally.

1 INTRODUCTION

The full-scale Russian invasion of Ukraine on

February 24, 2022, was the most momentous

development in the Russian-Ukrainian war since the

annexation of Crimea in 2014. This invasion, with

state and non-state actors meddling with the

information stored or transmitted, has been called the

first totalitarian Social Media war, or alternatively,

the first totalitarian cyber war and the first hacker

war.

It was obvious that both sides would take

advantage of the participatory Internet's enormous

capacity to launch public awareness campaigns and,

eventually, carry out their military goals (Smith,

2019). Social Media gave Ukraine the means to share

disjointed information about how the war was fought,

which in turn gave Internet users all around the world

a mosaic of informational data, establishing, at the

same time, the framework for requesting outer

assistance. On the other hand, Social Media was seen

https://orcid.org/0000-0001-6932-1513

https://orcid.org/0009-0008-9161-0416

by Russia as an additional medium for shaping and

disseminating the idealized narrative of the conflict.

Ukraine presented the incident as a brutal act of

war in general, portraying Russia as the aggressor

breaking international law and Ukraine as the

defendant attempting to reclaim the area that the

invader had taken. Nonetheless, Russia claimed that

the events were a deliberate military action to liberate

a Russian minority that had been "imprisoned" in the

Donetsk and Luhansk regions of Ukraine (Al Jazeera,

2022).

This study analyzes data from the

communications patterns spread worldwide by both

parties on Twitter (now known as X), a social

networking site with 432 million monthly active users

as of 2022 (Dixon, 2022), using open source software

packages. Twitter has always been a widely used

platform for deploying politics and diplomacy

(Twiplomacy) worldwide. By using data analysis

techniques as well as statistics, the study's goals were

to analyze the spoken communications exchanged

494

Vagianos, D. and Papatsas, T.

Twitter Metrical Data Analysis Using R: Twiplomacy in the Outbreak of the War in Ukraine.

DOI: 10.5220/0012812400003756

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 13th International Conference on Data Science, Technology and Applications (DATA 2024), pages 494-504

ISBN: 978-989-758-707-8; ISSN: 2184-285X

during the three months that followed Russia's

invasion of Ukraine on February 24, 2022, to

document the tactics used by both sides as well as the

degree of coordination in the way that different

accounts used the platform, and, lastly, to assess how

successful each side was in electronic diplomacy over

this field of confrontation.

2 RELATED WORK

Throughout their nearly two decades in cyberspace,

Social Media have been utilized to investigate social

concerns through the application of techniques from

the ever developing data science field. Furthermore,

a well-liked natural language processing (NLP)

method that academics have employed in a variety of

fields is content analysis. The method is frequently

applied in studies on Twitter because of the platform's

nature as a microblogging service.

Younis, for instance, employed the 'afinn'

dictionary approach to determine people's opinions

about data obtained from Twitter about two UK stores

(Asda and Tesco) over the 2014 Christmas season

(Younis, 2015). Similar text processing on Twitter

data were applied by Kabir et al. using R (Kabir et al.,

2018). By tracking word usage rates, they

demonstrated how positive and negative words

influenced respondents' overall sentiment in a variety

of qualitative surveys by using R over Twitter data.

Arun et al. analyzed tweets in India that discussed

people's opinions regarding the delegitimization

process using the Bing dictionary technique (Arun et

al., 2017). Saini et al. (2019) employed a similar

technique of categorizing impact of tweets

concerning healthcare and illnesses into ten groups

using the "nrc" lexicon.

Furthermore, R Studio and Gephi have shown to

be outstanding resources for a variety of data science

applications. For instance, Koutsoupias and Mikelis

used R Studio and text mining software to review

international relations materials in order to look for

recurring, related terms (Koutsoupias and Mikelis,

2021). Taking centrality into account when

computing their metrics, Wajahat et al. gathered data

from Netvizz's Facebook API and visualized

Facebook social networks linked to the official CNN

profile page using Gephi (Wajahat et al., 2020). Zhu

et al. conducted content analysis on a Reddit dataset

regarding the Russian-Ukrainian war using R studio

visualization techniques (Zhu et al., 2022). Using data

from the Reddit platform, Hanley et al. examined

Russian media narratives targeted at English-

speaking customers using the MPNet model and a

semantic search algorithm (Hanley et al., 2022).

Using specified keywords and hashtags to track

statistics, Haq et al. examined the text of around 1.6

million tweets obtained via the Twitter API during the

first week of escalation (Haq et al., 2022).

Using a bigger dataset (57.3 million tweets, 7.7

million users), Shevtsov et al. investigated the

frequency of tweets in the Russian-Ukrainian War

(Shevtsov et al., 2022). In an analysis of tweets from

December 31, 2021 to March 3, 2022 of the same war,

Agarwal et al. used the "bing" dictionary. Finally,

Džubur et al. employed the RoBERTa-LSTM

technique for sentiment analysis by studying hashtags

and users. They also mapped semantic, interactivity

networks of various profiles using Gephi for their

network analysis (Džubur et al., 2022).

3 METHODOLOGY

This study focused on the first 3 months after Russia's

invasion of Ukraine. This decision was made because

the situation in Ukraine was totally unstable at the

time, a fact that was reflected by the numerous tweets

that captured the overall tone of hostility. Thirty

Twitter accounts were selected for use, with fifteen

accounts for each country, due to the volume of data

that was accessible. The following criteria were used

to choose the accounts:

i. English written tweets. Tweets written in

Russian or Ukrainian might be directed towards

those who speak those languages, but tweets

written in English undoubtedly target a larger

audience.

ii. The account holder needs to be well-liked on

Twitter and a recognized expert on this conflict.

Politicians were carefully chosen for our sample

along with news media, political analysts,

consultants, ministries, and other prominent

figures with a sizable following basis and

impact.

iii. "Official accounts" or "affiliated media". With

consistent tweets in support of an account,

Twitter labels them as such.

Twitter Metrical Data Analysis Using R: Twiplomacy in the Outbreak of the War in Ukraine

495

Table 1: Selected Ukranian Twitter accounts Study Group.

Account Description

ZelenskyyUa

VolodymyrZelenskyy, President of

Ukraine

Ukraine Official Twitter account of Ukraine

lesiavasylenko Lesia Vasylenko, Ukrainian MP

Denys_Shmyhal

DenysShmyhal, Prime Minister of

Ukraine

StefanishynaO

OlgaStefanishyna, Deputy Prime

Minister of Ukraine

FedorovMykhailo

MykhailoFedorov, Deputy

President of Ukraine

DmytroKuleba

DmytroKuleba, Minister for

Foreign Affairs of Ukraine

oleksiireznikov

OleksiiReznikov, Minister of

Defence of Ukraine

otkachenkoua

TkachenkoOleksandr, Minister for

Culture of Ukraine

Podolyak_M

Advisor to the Office of the

President of Ukraine

NewVoiceUkraine

The top independent English-

lan

e news in Ukraine

MFA_Ukraine

Official Twitter account of the

Ministry of Foreign Affairs of

Ukraine

SergiyKyslytsya

SergiyKyslytsya, Representative of

Ukraine in the UN

UKRinUN

Official Twitter account for

Mission of Ukraine to the UN

Makeiev

OleksiiMakeiev, Ambassador of

Ukraine in Germany

From the Ukrainian side, the accounts in Table 1

were chosen based on the aforementioned criteria

Table 2: Selected Russian Twitter accounts Study Group.

Account Descri

tion

mfa_russia

Official Twitter account of the

Ministry of Foreign Affairs of

Russia

Official Twitter account of the

Russian Federation

KremlinRussia_E

Official Kremlin news from the

President of Russia

GovernmentRF

Official news of the Prime Minister

& the Russian Government

MID_Kaliningrad

Official Twitter account of the

Dele

ate in Kalinin

MedvedevRussiaE

DmitryMedvedev, Deputy

Chairman of the Security Council of

Russia

mod_russia

Official Twitter account of the

Russian Ministry of Defence

DnKornev

Russian military journalist and

logger in English

Amb_Ulyanov

MikhailUlyanov, Official

resentative of Russia in Vienna

Dpol_un

DmitryPolyanskiy, Official

resentative of Russia to the UN

A__Alimov

AlexanderAlimov, Official

Representative of Russia to the UN

RussiaUN

Official Twitter account of the

Russian Mission to the UN

FridrihShow

NadanaFridrikhson, Russian

ournalist in En

lish

RusMission_EU

Official Twitter account of the

Russian Mission to the EU

politblogme

MariaDubovikova, Russian

ournalist and anal

st in En

lish

The Russian side's accounts were chosen based on

the same standards (Table 2). It can be seen even at

this early stage of the investigation that relatively

more members of the Ukrainian presidential cabinet

had Twitter accounts. This pattern became more

noticeable when the study's scope was later expanded

to cover a larger spectrum of government personnel.

It should be mentioned that these accounts usually

tweeted in English even prior to the events of 2022. It

was more difficult to find Russian presidential office

officials—including Russian President Vladimir

Putin—who tweeted in English because many of

them either posted only in Russian or had no Twitter

account at all. Rather, Vladimir Putin seems to be in

charge of two accounts, "KremlinRussia" (which

tweets in Russian) and "KremlinRussia_E" (which

tweets in English), which function in tandem with the

profile of the president of Ukraine. Upon initial

observation, it was also noted that other Russian

government figures with profiles in both Russian and

English, shared information more often in English

than in Russian.

Lastly, this study focused on two sets of accounts:

collaborative journalists participating in the Russian-

Ukrainian war, as well as a variety of political

entities. Although these entities might not have

attended any of the focus groups, this initial selection

helped uncover more Twitter accounts owned by

significant figures engaged in the conflict. Finally,

since the time required to run these programs'

calculations grows exponentially with the amount of

input data, it was possible to conduct the

mathematical calculations with the available software

and equipment in reasonable time by keeping the

study's sample size limited to these thirty profiles.

Open source software was used for this

investigation. R Studio was the primary application

utilized because of its ease of use, data mining

capabilities and statistical calculations (Kumar and

Paul, 2016).

Table 3 lists the extra packages that were used

along with their purposes. Using a Twitter account,

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developer account permissions were granted in order

to get the datasets for analysis from the Twitter

platform using the “rtweet” package. The search

results were restricted to the designated time frame.

An open source network visualization tool named

Gephi was also utilized (Bastian et al., 2009), which

allowed for the mapping of the interactivity network

between the chosen accounts and the identification of

the most significant nodes, based on how frequently

they appeared in tweets.

Table 3: Selected R Studio packages.

Package Description

rtweet a Main data minin

tool from Twitte

tidyverse

Set of functions for graphing and

data processing

Natural language text mining

functions

(

)

dply

Data manipulation functions

TSstudio Time series visualisation functions

forestmangr

Functions for data frames and

calculations

ggplot2

Functions for creating customized

3.1 Tweets’ Text Analysis

Using the R Studio "tm" package, Twitter posts were

converted into .txt files to make it feasible for

unnecessary data to be removed (Feinerer et al.,

2008). The following ensuing interventions were

executed:

i. All uppercase characters were changed into

lowercase, due to the software's sensitivity to

this issue.

ii. All non-English terms found in the tweets from

the chosen accounts were disregarded in this

analysis.

iii. Punctuation marks such dots, commas,

exclamation points, etc. were removed from the

texts under study.

iv. Numbers were removed from the analysis. They

were only taken into account when they

contained crucial information in combination

with nearby words. For instance, the number

"170" is semantically useless but when paired

with the words "casualties" or "refugees," it

might provide important information.

v. Symbols like €, £, TM, ", â, /, @, ®, _, -, and

others that were present in emojis, hyperlinks

etc. were eliminated. In order to achieve this, a

function in R was developed to change these

superfluous symbols into spaces, which were

subsequently removed.

vi. Prepositions, conjunctions, Emojis, Cyrillic

sentences and numbers made up the majority of

the words that were eliminated.

vii. Terms with comparable meanings were grouped

together without using any dictionary. For

instance, the terms "ukrainian," "ukrainians,"

and "ukraines," were transformed into "ukraine"

in a manner similar to that of symbols;

conversely, the words "neonazi," "neonazis,"

"nazism," "nationalists," and "azov" were

converted to "nazi," and so forth (depending on

their meaning). By converting these terms, the

software accurately categorized the words rather

than breaking them up into smaller groupings.

Multiword expressions and word embeddings

were not considered in this approach.

viii. Since the gaps had no bearing on the text, they

were removed.

To determine the minimal frequency of word

occurrence, the revised text was used. By converting

the text files into a tabular format and presenting the

term in one column and its frequency in the adjacent

column, the words were summed up in two columns.

These tables were sorted in decreasing order using the

"ggplot2" package, and each country's most frequent

words were displayed as a bar chart that showed the

overall volume and ranking of these words.

3.2 Twitter Interactivity Networks

The next stage was to mine the data required to build

each focus group's interactivity network. Using the

"dplyr" software, two .csv files were made for each

nation (—one containing the connections and the

other containing the nodes). These files were used as

Gephi's input data. Then, in order to build the

interactivity networks in Gephi, the following

parameterizations were applied to guarantee a better

visual presentation and to increase the accuracy of the

computations:

i. Self-loops: A lot of accounts frequently made

self-referential statements or retweeted their

own tweets. As a result, the network nodes' in-

degree weight grew, making them more

significant than they are. These cases ought to

be disregarded.

ii. Network directionality: it was important to

specify the references’ direction.

iii. Nodes were scaled by the weight of the

weighted in-degree references: this allowed

for identifying significant nodes (Ayyappan et

al., 2016).

Twitter Metrical Data Analysis Using R: Twiplomacy in the Outbreak of the War in Ukraine

497

iv. The modularity community detection algorithm

was used to highlight the accounts’ communities

in the networks, based on their connections

(Newman, 2006).

v. Yifan Hu network creation: this method

highlighted the important central accounts in the

network and allowed for its effective and clear

visualization (Hu, 2004).

vi. Each network's visualisation was done in two

stages, which guaranteed that the interactivity

networks would be effectively visualized. The

labels of the fifteen selected profiles remained

after the first stage, which involved the removal

of nodes with a single incoming reference (in-

degree=1). The visible accounts and the labels

of the accounts with the highest degree of

incoming references were plotted in the second

stage, after the labels displayed in the first stage

were hidden and the filter was increased by one

(2 ≤ in-degree).

Four schemes were developed, two for each focus

group, using these criteria.

4 APPLICATION OF THE

METHOD AND EXTRACTION

OF RESULTS

The first significant finding in this research was that

there were fewer tweets by the Russian side during

the analysis period. Specifically, the Ukrainian side

released twenty-five percent more content. The top 50

terms used on each side during these posts are shown

in Figures 1 and 2.

It can be observed that terms like "war,"

"support," "world," "aggression" of the opposing

side, "Kiev," and appeals for assistance to

"standwithukraine" are among the top ten terms used

on the Ukrainian side. 'War' was the third most

frequent term.

From this observation, one can conclude that

Ukraine's posts aim to inform and sensitize the world

by emphasizing the aggression of the invader and

therefore make direct or indirect appeals for support

by the international community.

Comparably, "Nazi" is the third most used word

in Russia's top ten list of words, which also includes

"military," "war," "Kiev," and "civilians" (Figure 2).

The latter provides a clear image of the Russian side's

communication strategy regarding the reasons behind

the invasion, as evidenced by the tweets it posted.

Figure 1: 50 Most frequently used words in the tweets of

the Ukrainian side.

In Figure 1, one can see that the Ukrainian side

utilizes phrases with a warlike meaning, such as

"invasion," "defence," "forces," "peace," "killed,"

"crimes," and "children," in order to incite animosity

toward the aggressor. Additionally, the terms

"resolution," "sanctions," and "meeting" signal to the

global community that action is needed.

Geographically speaking, the terms Mariupol and

Kiev feature prominently on the list; references to

Europe are also common, either in regard to their

potential support or their role in mediating the crisis.

In an effort to start movements in favor of Ukraine,

the hashtags "stoprussiaaggression" and

"standwithukraine," which rank 10th and 11th

respectively, were also widely used. Specifically, on

February 25, 2022, the hashtag "standwithukraine" was

included in over 40000 tweets (GetDayTrends, 2022).

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Figure 2: 50 Most frequently used words in the tweets of

the Russian side.

One may observe that there are differences in the

subject areas and style by comparing Figures 1 and 2.

"West" was the eleventh most frequently used word,

highlighting Russia's position on the political front

established by the West. Political terms and names

like "Putin", "Lavrov", "Zakharova", "Nebenzia", and

"Sergey" are used in large groups of tweets, along

with terms like "international", "countries", "states",

and "NATO". This shows how the Russian side uses

Twitter for news, information, and e-diplomacy.

Additionally, the descriptions of the events are given

a more official colour by the terms "operations,"

"security," "situation," "civilians," and

"humanitarian," which contrasts sharply with how the

opposite belligerent side makes these same

descriptions. The towns of Mariupol and Kiev are the

most commonly stated geographical regions here,

although "Donbass" has also continued to be

mentioned regularly, presumably as a result of the

events that occurred there. Generally speaking, the

Russian side uses more words related to a wider range

of mostly informative content, whereas the Ukrainian

side alternates between political and humanitarian

news and war narratives with a specific colour tone.

The official “DmytroKuleba” account of the

Minister for Foreign Affairs of Ukraine, the official

“ZelenskyyUa” account of President Volodymyr

(a)

(b)

(c)

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499

(d)

Figure 3: Frequency of posts from 4 selected Ukrainian

Twitter accounts.

Zelenskyy of Ukraine, the official “MFA_Ukraine”

account of the Ministry of Foreign Affairs of Ukraine,

and the “Ukraine” account, correspond to the four

charts of Figure 3 that indicate the frequencies and

total posts of four representative accounts of the

Ukrainian side during the period under review. The

four charts highlight the high activity in the initial

days of the invasion as well as the constant high

frequency of posts in this time frame. Furthermore,

the four accounts' variance lines visually exhibit

qualitative similarities, indicating a probable

coordination in their activities and, as a result, a

methodical posting strategy.

The four charts in Figure 4 depict the indicative

frequencies and total posts of four representative

Russian side accounts during the same period: the

Russian Ministry of Foreign Affairs' “mfa_russia”

account, the Ministry of Defense's “mod_russia”

account, the Representative's “MID_Kaliningrad”

account, and the Russian Mission to the UN's

“RussiaUN” account. Comparing the four charts, it

(a)

(b)

(c)

(d)

Figure 4: Frequency of posts from 4 selected Russian

Twitter accounts.

can be noted that the frequency of posts is lower than

the ones on the Ukrainian side. Thus, this quantitative

approach demonstrates that the Ukrainian side's

engagement has been more rigorous as well as more

intensive, which is consistent with the greater

influence that Ukraine appears to have had—both

during and after the invasion—on Social Media, a fact

that has also been reported by the media generally.

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Note that the four charts of figure 4 have the

maximum qualitative similarity of their curve, much

like the four charts of figure 3 that were selected to be

given here. The frequency of articles varies

significantly amongst the 15 Russian accounts,

perhaps because of the relatively poor level of

coordination between the political and journalistic

organizations managing these accounts. On the other

hand, Ukrainian account holders exhibit a stronger

link with respect to a widely recognized pattern of

posting rate, topic selection, and even word choice in

terms of posts throughout this time frame.

The interactivity network graphs of the Ukrainian

group's first and second stage accounts, as previously

mentioned in the research methodology (modularity

detection algorithm), are displayed in figures 5 and 6.

The first stage figure 5 shows that the largest and most

concentrated nodes of the account network were

"DmytroKuleba", "MFA_Ukraine", and

"ZelenskyyUa" based on the interactions and their

content. The first two are red, indicating that they are

part of the same community, and this is always the

case based on the modularity detection algorithm that

was used. Given that Kuleba is the Ukraine's foreign

minister, this appears to support the algorithm's

accuracy. Conversely, the accounts

"FedorovMykhailo","otkachenkoua", and "Makeiev"

appear to have the most dissimilar profiles with their

common references and indicated communities, even

if they are all governmental accounts. The network

exhibits consistency in terms of content and

relationships, as seen by the other accounts that were

noticed and looked to be key to it. They also appeared

to be located close to one another.

Figure 5: 1st Stage interactivity network of Ukrainian side

accounts.

Figure 6: 2

Stage interactivity network of Ukrainian side

accounts.

This research focused on the accounts that were not

highlighted in the first stage and displayed the most

significant Twitter accounts (Table 4) based on

content and the quantity of incoming mentions in the

stage 2 interactivity network (Figure 5). Thus, two

clusters are formed. While the second was directed at

nation-state leaders and the European Union, the first

was focused on the United Nations. The first group

appeared to represent the culmination of two

communities: one associated with international

affairs (red) and one with the UN (yellow). In the

second, several communities were combined and

given a central position inside the network.

Table 4: Accounts highlighted during the creation of the

2nd stage of the Ukrainian side's interactivity network.

Screen name

Description

POTUS

Joe Biden, President of the United

States

BorisJohnson

Boris Johnson, Prime Minister of the

United Kingdom

VenediktovalV

Ambassador of Ukraine to the Swiss

Confederation

SecBlinken

Anthony Blinken, Secretary of State

of the United States

eucopresident

Charles Michel, President of the

European Council

Official account of the United Nations

AndrzejDuda

Andrzej Duda, President of Poland

NATO

Official account of the North Atlantic

Treaty Organisation

antonioguterres

António Guterres, Secretary

General of the United Nations

EU_Commission

Official account of the European

Commission

Twitter Metrical Data Analysis Using R: Twiplomacy in the Outbreak of the War in Ukraine

501

Table 4: Accounts highlighted during the creation of the 2n

stage of the Ukrainian side's interactivity network(cont.).

vonderleyen

Ursula Von Der Leyen, President of

the European Commission

MorawieckiM

Mateusz Morawiecki, Prime Minister

of Polan

JustinTrudeau

JustinTrudeau, Prime Minister of

Canada

OlegNikolenko_

Spokesman of the Ministry of

Foreign Affairs of Ukraine

UKUN_NewYork

Official account of the United

Kingdom's UN office in New Yor

EmineDzeppar

First Deputy Foreign Minister of

Ukraine

AlMissionUN

Albania's official account in the

United Nations in New Yor

irishmissionun

Ireland's official account in the

United Nations in New Yor

USambUN

US Ambassador Linda Thomas-

Greenfield to the UN

LithuaniaUNNY

Lithuania's official account in the

United Nations in New Yor

UNspokesperson

Official account of the Secretary

General Office of the UN

NorwayUN

Norway's official account in the

United Nations in New Yor

EUatUN

Delegation of the European Union in

the UN in New Yor

franceonu

France official account in the United

Nations in New Yor

elonmus

Εlon Μusk, Entrepreneur and investor

UNspokesperson

Official account of the Secretary

General Office of the UN

The interactivity networks for the Russian group's

first and second stage accounts are displayed in

figures 7 and 8.

Figure 7: 1

Stage interactivity network of Russian side

accounts.

Figure 8: 2

Stage interactivity network of Russian side

accounts.

The network is noticeably less compact in the

first stage figure 7, with many of the observed

accounts situated far from the centre and forming

their own communities. Unexpectedly,

"MedvedevRussiaE" is the most estranged account; it

has no connections to the entire interactivity network

and no relevant community. Being "satellites" to the

main network, the "DnKornev," "FridrihShow," and

"politblogme" accounts have their own communities,

while the "Russia" account is not particularly

connected to the other profiles (bottom left of the

graph of Figure 7). Despite being a part of a huge

community, the "GovernmentRF" account is not

regarded as a significant node in that network. The

network's core is made up of the other accounts, with

"Amb_Ulyanov" displaying a sizable community and

several connections to other accounts.

The most significant accounts that surfaced

(Table 5) once more established two clusters in the

Russian second stage interactivity network (Figure 8).

With independent or state journalists, the first was

focused on journalism, and the second was focused

on international affairs through embassies.

Because of its central location in the original

interactivity network, the second community was a

composite of several dispersed communities, while

the first partially contained three communities (green,

yellow, and red). Furthermore, two prominent profile

accounts regarding the International Atomic Energy

Agency were noted in the centre ("iaeaorg" and

"rafaelmgrossi"). Their presence in this network can

be correlated with the content analysis of the posts,

since "nuclear" was one of the most frequently used

terms in the Russian group (Figure 2).

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Table 5: Accounts highlighted during the creation of the

2nd stage of the Ukrainian side's interactivity network.

Screen name Description

ejmalrai Journalist, war correspondent in

Asia

thesiriusreport Independent International

Relations Anal

sts

rihimedhurst Syro-British freelance journalist

wyattreed13 Russian war correspondent

NinaByzantina Russian journalist and international

relations anal

VeraVanHorne Russian-Canadian journalist

AlanRMcleod Independent Journalist at

"MintPressNews"

MaxBlumenthal Independent journalist of

"TheGre

Zonenews"

aaronjmate Journalist and Podcaster at "The

Grey Zone news"

EvaKBarlett Independent journalist in Donbas

SpokespersonCHN FA Assistant Minister and

representative of China

iaeaorg International Atomic Energy

enc

(

IAEA

)

rafaelmgrossi Director General of the IAEA

RF_OSCE Mission of the Russian Federation

to the OSCE

RussianEmbassyC The Russian Embassy in Canada's

Official Account

EmbassyofRussia The Russian Embassy in South

Africa

armscontrol_rus Negotiations delegation on security

and arms control

RussianEmbassy Official Account of the Russian

Embassy in the UK, London

PMSimferopol Official Account of the Ministry of

Foreign Affairs of Russia in Crimea

RusEmbUSA Official Account of the Russian

Embass

in the USA

mission_russian Russian mission to the United

Nations and International

Organizations

MID_RF Official account of the Russian

Forei

n Ministr

in Grozn

RusEmbassyMinsk Official Account of the Russian

Embassy in Belarus

RusAmbCambodia Official Account of the Russian

Embassy in Cambodia

mission_rf Russian delegation to international

anisations in Vienna

Social Media is well on the way to disrupt the

traditional channels and methods of diplomacy (Pop,

2018). Analysis like the one above demonstrate the

potential of Social Media and online communities in

International Relations, Therefore, the results above

can provide useful information that can be further

scientifically interpreted by IR scientists.

5 CONCLUSIONS AND FUTURE

WORK

The study concentrated on Twitter's impact during the

initial three months following Russia's invasion of

Ukraine. Fifteen official accounts, one from each of

the fighting factions, were chosen as exemplary

examples, and their content and frequency of postings

were analysed using exclusively Open Source

software. The study's findings demonstrated how

successfully Ukraine used Twitter to spread

information and increase awareness by adding words

to content that highlighted the invader’s aggression

and by creating trending hashtags that made direct or

indirect appeals for international support. This

finding coincided with the general media image that

has been shaped during these 3 months and has been

delivered to the international community. News on

politics or humanitarian issues as well as combat

stories were among the Ukrainian themes. However,

Russia employed a wider variety of mostly

informative tweets on Twitter along with service-

oriented content for news, information, and electronic

diplomacy.

In terms of posting frequency, account managers

in Ukraine were found to have demonstrated a faster

pace, improved inter-administrator’s collaboration,

and a more methodical approach, which was evident

in both the posting rate pattern as well as the word and

topic selection.

Focusing on the interactivity networks, the

Ukrainian side seemed to have used Twitter to get in

touch with the UN and other international or

European organizations. In contrast, the Russian

side's networking revealed that it aimed to establish a

channel of communication with Russian embassies

and news organizations affiliated with the Russian

state in order to disseminate information favourable

to the Russian regime and self-report on events that

benefited it.

The investigation above demonstrated how

Ukraine used Twitter more consistently and,

eventually, more successfully throughout the first

three months of the conflict, appearing to have met its

objectives by employing the right layered tactics.

This was clear from the three axes—text analysis,

posting frequency, and interactivity networks—over

which the current study of this sample was conducted.

More data can be added to this area of study to

produce results that are more representative and

generalized, but doing so will necessitate more

sophisticated computing hardware that can handle the

increasing computational demands.

Twitter Metrical Data Analysis Using R: Twiplomacy in the Outbreak of the War in Ukraine

503

Social networks measurements performed in this

analysis have provided useful datasets that can be

analysed further using a variety of statistical methods.

Moreover, other research techniques that can be

applied include the use of semantically more effective

NLP approaches combined with sentiment analysis of

posting content (Mohammad, 2015) and application

of multivariate statistical processing of Social Media

data. To draw more thorough conclusions or examine

trends in various stages of the conflict, these methods

can be applied to other Social Media platforms as well

as over more extended time frames.

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