THE USE OF SOCIAL MEDIA DURING THE 2014 CRISIS IN UKRAINE ANATOLIY GRUZD @GRUZD Carolinas ASIST Workshop on Social Media Analysis and Its Application in Research and Practice October 19, 2015 Associate Professor, Ted Rogers School of Management Director of the Social Media Lab Ryerson University
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The Use of Social Media during the 2014 Crisis In Ukraine
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THE USE OF SOCIAL MEDIA
DURING THE 2014 CRISIS IN UKRAINE
ANATOLIY GRUZD
@GRUZD
Carolinas ASIST Workshop on Social Media Analysis and Its Application in Research and Practice
October 19, 2015
Associate Professor, Ted Rogers School of Management
Director of the Social Media Lab
Ryerson University
Social Media Lab, Ryerson University (Toronto, Canada)
@gruzd 2
Social Media Support Social Activism and Political Engagement
#OccupyGezi Supporters in Victoria, BC
From this…
Photo credit: Anatoliy Gruzd
In the Social Media Lab,we’re interesting in studying how social media ….
@gruzd 2014 EuroMaidan Revolution
Photo credit: Karl Schönswetter
Social Media Help to Organize Social Movements
… to this
#OccupyGezi: Gezi Parki Protest, Turkey (2013)
4
In the Social Media Lab,we’re interesting in studying how social media ….
@gruzd 2014 EuroMaidan Revolution
Today’s focus:
2014 EuroMaidan Revolution | Revolution of Dignity
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"2014-02-21 11-04 Euromaidan in Kiev" by Amakuha. Licensed under CC BY-SA 3.0 via Wikimedia
@gruzd
Outline
• Background
• Review of Social Media Platforms
• VK (Vkontakte) Use
• Conclusions
6@gruzd
Today’s focus:
2014 EuroMaidan Revolution | Revolution of Dignity
7
"2014-02-21 11-04 Euromaidan in Kiev" by Amakuha. Licensed under CC BY-SA 3.0 via Wikimedia
November 21, 2013 - Ukraine gov. suspended
the trade & association agreement with EU
@gruzd
February 18-19, 2014: Protests in Kyiv (capital) Turned Deadly
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source: http://liveuamap.com
RUSSIA
UKRAINE
@gruzd 2014 EuroMaidan Revolution
February 18-19, 2014: Protests in Kyiv Turned Deadly
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"Barricade line separating interior troops and protesters. Clashes in Kyiv, Ukraine. Events of February 18, 2014-2" by Mstyslav Chernov/Unframe/http://www.unframe.com/ - Licensed under CC
BY-SA 3.0 via Wikimedia Commons
@gruzd 2014 EuroMaidan Revolution
February 18-19, 2014: Protests in Kyiv Turned Deadly
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"Euromaidan in Kiev 2014-02-19 12-06" by Amakuha. Licensed under CC BY-SA 3.0 via Wikimedia Commons
@gruzd 2014 EuroMaidan Revolution
February-March, 2014: Pro & Anti-Maidan Protests Spread Across
Ukraine
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source: http://liveuamap.com
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February-April 2014: A wave of Anti-Maidan protests in South-East
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Photo credit: Andriy Makukha. Licensed under CC BY-SA 3.0 via Wikimedia Commons
2014 EuroMaidan Revolution
March 2014: Annexation of Crimea by Russia
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source: http://liveuamap.com
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April 2014: Anti-Terrorist Operation (ATO) against pro-Russian Self-proclaimed
About Vkontakte – #1 Social Networking Website in Ukraine
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source: http://en.wikipedia.org
@gruzd 2014 EuroMaidan Revolution
Example: VK Group User Interface – Posts, Likes, Comments…
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Example: VK Group User Interface – Discussion board, Links & Media Files…
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Pro-Maidan (pro-Western) & Anti-Maidan (pro-Russian) Groups on VK
~Over 3,500 groups with membership from 100K+ to under 10 members
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Anti-Maidan groupPro-Maidan group
@gruzd 2014 EuroMaidan Revolution
Our Research Focus on Online Social Networks
http://www.visualcomplexity.com/vc
Friends’ networks (Facebook,
Twitter, Google+, etc…)
Forum networks
Blog networks
Networks of like-minded people
(YouTube, Flickr, etc…)
@gruzd 332014 EuroMaidan Revolution
1) Represent data as a network
Nodes = People
Edges /Ties = Relations (ex. Who is a friend with whom,
Who replies to whom, etc.)
•2) Apply Social Network Analysis (SNA)
We Study Online Groups from a Network Perspective
@gruzd 342014 EuroMaidan Revolution
• Reduce the large quantity of data into a
more concise representation
• Makes it easier to understand what is
going on in a group
Advantages of Using Social Network Analysis
Once the network is discovered,
we can find out:
• How do people interact with each other,
• Who are the most/least active members of a group,
• Who is influential in a group,
• Who is susceptible to being influenced, etc…
@gruzd 352014 EuroMaidan Revolution
Different Friends’ Networks: What can we learn from them?
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Anti-Maidan groupPro-Maidan group
@gruzd 2014 EuroMaidan Revolution
Different Friends’ Networks: What can we learn from them?
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Anti-Maidan groupPro-Maidan group
@gruzd 2014 EuroMaidan Revolution
Study Objectives
• Establish baseline measures to interpret emerging social structures of
online activist groups.
• In particular, we ask if there are any observable structural differences or
similarities in social networks formed by VK groups in the two opposing
camps?
• If there are differences, we would like to know whether network properties
alone might be able to suggest a group’s political ideology or other
characteristics.
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Data Collection
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PRO1 PRO2 ANTI1 ANTI2
Num. of Nodes 141,542 96,402 60,506 69,029
Num. of Connections 338,344 221,452 280,678 192,273
• Used VK Public API
• Communities – information about groups and group members
• Wall – posts and comments
• Likes – “likes” that members and visitors leave on posts
• Friends – group members’ friendship relations
• Data collection: 2 most popular (public!) Pro-Maidan and Anti-Maidan groups
• Period: February 18 – May 25, 2015
@gruzd 2014 EuroMaidan Revolution
Method
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• Social Network Analysis
• SNA measures (e.g., centrality, density, network diameter)
• Exponential Random Graph Modeling (ERGM) – test association tendencies
• Walktrap Community Detection algorithm - identify and describe highly connected
subgroups
• Network Visualization using LGL (Large Graph Layout)
• Manual Content Analysis of
• Group pages and posts
• Sample of public user profiles
• Research software
• Package R (libraries statnet and igraph)
• Tableau for visual analytics
@gruzd 2014 EuroMaidan Revolution
VK Group Example – Pro Maidan #1Friends’ Network (>140k members)
• Formed in early April 2014 to support
Maidan and Antiterrorist Operation
(ATO)
Yellow – users from Ukraine; Red – from Russia; Green – other countries;The layout algorithm is LGL (Large Graph Layout). Isolated nodes are not visible.
0.1
0.2
0.2
0.4
0.4
1.0
1.6
10.2
14.8
69.4
1
2
3
4
5
6
7
8
9
10
0.0 20.0 40.0 60.0 80.0
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Subgroup 3
Subgroup 42
VK Group Example – Pro Maidan #1 “Walktrap” Community Detection
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Subgroup 3
Subgroup 42
Subgroup 4
Most politically
active
VK Group Example – Pro Maidan #1 “Walktrap” Community Detection
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Crimean Tatars
Marketing,
Spam
@gruzd 2014 EuroMaidan Revolution
VK Group Example – Pro Maidan #2Friends’ Network (>96k members)
Subgroup 8Subgroup 96
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VK Group Example – Pro Maidan #2Friends’ Network (>96k members)
Subgroups 8
Marketing &
spam
Subgroup 96
Marketing & spam
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VK Group Example – Pro Maidan #2Friends’ Network
Subgroup 8Subgroup 96
Subgroup 12
Subgroup 3
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Subgroup 6
Subgroup 1
VK Group Example –Anti Maidan #1“Walktrap” Community Detection
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Subgroup 6
Politically active
users
Subgroup 1:
mixed
“marketing”,
politically
active
VK Group Example –Anti Maidan #1“Walktrap” Community Detection
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Subgroup 6
Subgroup 1
64% from
Donetsk
VK Group Example –Anti Maidan #1“Walktrap” Community Detection
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VK Group Example – Anti-Maidan #2
Friends’ Network (69K members)
One densely connected cluster - suggesting a stronger
agreement among group members from both Ukraine
and Russia
0.1
0.1
0.1
0.3
0.4
0.5
1.1
11.2
34.4
50.0
0.0 10.0 20.0 30.0 40.0 50.0 60.0
1
2
3
4
5
6
7
8
9
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• In existence since 2011, focused on
Anti-American & Pro-Russia discussions.
• Since the events on Maidan in early
2014, shifted its focus to support Anti-
Maidan activism & the two self-
proclaimed republics – Donetsk and
Lugansk People's Republics.
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VK Group Example – Anti-Maidan #2
Friends’ Network (69K members)
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VK Group Example – Anti-Maidan #2
Friends’ Network (69K members)
Politically active
accounts
Politically active
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Conclusions - Comparing Groups across Political Divide
PRO1 PRO2 ANTI1 ANTI2
Number of Nodes 141,542 96,402 60,506 69,029
Number of Connections 338,344 221,452 280,678 192,273
Network Diameter 19 19 16 21
Clustering Coefficient 0.08 0.09 0.09 0.13
Degree Centralization 0.0048 0.0073 0.0815 0.0260
Modularity Index 0.58 0.58 0.42 0.24
%Users from Ukraine 69 73 57 34
%Users from Russia 15 10 30 50
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Conclusions - Geography Matters!
• Although all four groups included
people from both Ukraine and
Russia, the ERGM models
confirmed the tendency of group
members to friend others in the
same country.
• Furthermore, we also observed
homophily among users from
the same city for the top-10 cities
with the most number of VK users
in all groups
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Online social networks likely represent local and potentially
pre-existing social networks
"Euromaidan Protests" by Lvivske, NickK - Sources for particular cities are given at w:uk:Євромайдан
у регіонах України. Licensed under CC BY-SA 3.0 via Wikimedia Commons
@gruzd 2014 EuroMaidan Revolution
Conclusions - Comparing Groups across Political Divide
PRO1 PRO2 ANTI1 ANTI2
Number of Nodes 141,542 96,402 60,506 69,029
Number of Connections 338,344 221,452 280,678 192,273
Network Diameter 19 19 16 21
Clustering Coefficient 0.08 0.09 0.09 0.13
Degree Centralization 0.0048 0.0073 0.0815 0.0260
Modularity Index 0.58 0.58 0.42 0.24
%Users from Ukraine 69 73 57 34
%Users from Russia 15 10 30 50
55@gruzd 2014 EuroMaidan Revolution
Conclusions - Position of Spam & Marketing Accounts
• In the PRO1 and PRO2 groups, spam & marketing accounts appeared to be organized in a densely connected subgroup with high degree centrality values and low user engagement
• It is important to differentiate spam & marketing accounts from group supporters who might also be located in smaller, somewhat isolated subgroups because of their minority status in the group (e.g., Crimean Tatars in PRO1)
• Interestingly, this pattern was not observed in the ANTI1 or ANTI2 groups.
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Different Friends’ Networks: What can we learn from them?
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Pro-Maidan Groups Anti-Maidan Groups
Study Objectives (recap):
• Establish baseline measures to interpret emerging social structures of online activist groups.
• In particular, we ask if there are any observable structural differences or similarities in social networks formed by VK groups in the two opposing camps?
• If there are differences, we would like to know whether network properties alone might be able to suggest a group’s political ideology or other characteristics.
@gruzd 2014 EuroMaidan Revolution
Different Friends’ Networks: What can we learn from them?
58
Pro-Maidan Groups Anti-Maidan Groups
Conclusions:
• Group-level indicators, such as degree centralization, modularity index and average engagement level, could help to classify and describe groups based on their network properties and structures.
• BUT: SNA alone could not explain the structural differences between the four networks we examined.
• SOLUTION: a combination of SNA, visualization and community detection algorithm, coupled with a manual content analysis of a sample of group messages and user profiles is an effective approach to study the underlying social structures of online activist groups.
@gruzd 2014 EuroMaidan Revolution
Photo credit: Karl Schönswetter
#OccupyGezi: Gezi Parki Protest, Turkey (2013)
59
#OccupyGezi Supporters in Victoria, BC
?
Study Implications: Can we predict successful groups? Can we predict group’s longevity?
@gruzd 2014 EuroMaidan Revolution
Future Work
• Explore the roles of isolates in the
group
– lurkers, spammers, trolls, or fake
accounts?
60
?
• Investigate how people from
countries other than Ukraine
and Russia participated in these
groups
@gruzd 2014 EuroMaidan Revolution
Acknowledgments
• We thank Emad Khazraee, Dmitri Tsyganov, Andrea Kampen,
Philip Mai, and Elizabeth Dubois for their help in preparing this
article.
61@gruzd 2014 EuroMaidan Revolution
THE USE OF SOCIAL MEDIA
DURING THE 2014 CRISIS IN UKRAINE
ANATOLIY GRUZD
@GRUZD
Carolinas ASIST Workshop on Social Media Analysis and Its Application in Research and Practice
October 19, 2015
Associate Professor, Ted Rogers School of Management