Keynote at the Second International Symposium on Spatiotemporal Computing (ISSC 2017), August 7th – 9th, 2017 at Harvard University, Cambridge, Massachusetts

1. STUDYING ONLINE & OFFLINE COMMUNITIES THROUGH
THE PRISM OF SOCIAL MEDIA DATA
ANATOLIYGRUZD (@GRUZD)
Canada Research Chair in Social Media Data Stewardship
Associate Professor, Ted Rogers School of Management
Director of the Social Media Lab
Ryerson University

2. We are an interdisciplinary academic research laboratory

3. #OccupyGezi Supporters in Victoria, BC
From this…
Photo credit: Anatoliy Gruzd
In the Social Media Lab,
we study how social
media ….
Anatoliy Gruzd
How social media
can support
online communities,
social activism &
political engagement?

4. Photo credit: Karl Schönswetter
… to this
#OccupyGezi: Gezi Parki Protest, Turkey (2013)
5
In the Social Media Lab,
we study how social
media ….
How social media
can support
online communities,
social activism &
political engagement?

5. 2B
users
400M
users
300M
users
Growth of Social Media Data
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6. Decision Making
in domains such as Politics, Health Care and Education
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+ More Ways to Access Social Media Data
Public
APIs
Data
Resellers
Self-
collected
/reported

7. Data -> Visualizations -> Understanding
How to Make Sense of Social Media Data?
Data and Visual Analytics
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8. How to Make Sense of Social Media Data?
Spatiotemporal Computing
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9. How to Make Sense of Social Media Data?
Spatiotemporal Computing
Geography of
Twitter Networks
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10. How to Make Sense of Social Media Data?
Spatiotemporal Computing & Text Mining
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11. How to Make Sense of Social Media Data?
Spatiotemporal Computing & Text Mining
Source: http://www.fenuxe.com/tag/geo-coded
Tracking Hate Speech on Twitter
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12. Key Points
1. Social Media Data is a good proxy to study online and offline social
interactions
2. Social Network Analysis is an effective method to analyze social
media data
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13. Outline
• Background – Social Media & Social Network Analysis
• Social Media Use during the 2014 EuroMaidan Revolution
• Case of VKontakte
• Case of Twitter
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14. 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)
Examining Social Media Data from a Network Perspective
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15. Studying Online Social Networks
http://www.visualcomplexity.com/vc
Forum networks
Blog networks
Friends’ networks (Facebook,
Twitter, Google+, etc…)
Networks of like-minded people
(YouTube, Flickr, etc…)
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16. Advantages of Using Social Network Analysis (SNA)
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…
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17. Outline
• Background
• Social Media Use during the 2014 EuroMaidan Revolution
• Case of VKontakte
• Case of Twitter
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18. Background:
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
November 21, 2013 - Ukraine gov. suspended
the trade & association agreement with EU

19. February 18-19, 2014: Protests in Kyiv (capital) Turned Deadly
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source: http://liveuamap.com
RUSSIA
UKRAINE

20. 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

21. 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

22. February-March, 2014: Pro & Anti-Maidan Protests SpreadAcross
Ukraine
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source: http://liveuamap.com

23. February-April 2014:Awave 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

24. March 2014: Annexation of Crimea by Russia
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source: http://liveuamap.com

25. April 2014:Anti-Terrorist Operation (ATO) against pro-Russian Self-proclaimed
republics: Donetsk People’s Republic (DPR) & Lugansk People’s Republic (LPR)
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source: http://liveuamap.com

26. Outline
• Background - SNA
• Social Media Use during the 2014 EuroMaidan Revolution
• Case of VKontakte
• Case of Twitter
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27. This part is based on
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Gruzd, A. & Tsyganova, K. (2015). Information Wars and Online Activism
During the 2013/2014 Crisis in Ukraine: Examining the Social Structures
of Pro- and Anti-Maidan Groups. Policy & Internet 7(2).
DOI: 10.1002/poi3.91
Gruzd, A. & O’Bright, B. (2017) Big Data and Political Science: The Case of
VKontakte and the 2014 Euromaidan Revolution in Ukraine. In Sloan, L.,
& Quan-Haase, A. (Eds.). The SAGE Handbook of Social Media Research
Methods.

28. About Vkontakte – #1 Social Networking Website in Ukraine
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source: http://en.wikipedia.org

29. Example: VK Group User Interface – Posts, Likes, Comments…
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30. Example: VK Group User Interface – Discussion board, Links & Media Files…
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31. Data Collection
• 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
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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

32. Method
• 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
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33. Different Friends’ Networks: What can we learn from them?
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Anti-Maidan groupPro-Maidan group

34. • 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.
VK Group Example – Pro Maidan #1 (Pro-Western)
Friends’Network (>140k members)
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35. • Formed in early April 2014 to support
Maidan and Antiterrorist Operation
(ATO)
VK Group Example – Pro Maidan #1 (Pro-Western)
Friends’Network (>140k members)
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Crimean
Tatars
Spam
(Marketing)
“Walktrap” Community Detection

36. Subgroup 1
64% from
Donetsk
VK Group Example –Anti Maidan #1
“Walktrap” Community Detection
58@gruzd 2014 EuroMaidan Revolution
• The group’s focus to support Anti-
Maidan activism & the two self-
proclaimed republics – Donetsk and
Lugansk People's Republics.

37. VKontakte Conclusions - Geography Matters!
• Although all four groups included
people from both Ukraine and
Russia, the statistical 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

38. Research Questions based on
Country-to-Country Friendship Connections
1. Does international political rhetoric between states reflected in online
networks, groups, and interaction?
2. What impact does temporary migration patterns, including international
students, have on the demographic characteristics of online groups,?
3. Do expatriate communities in destination states have a demonstrable impact
on the message content, user demographics, or other observable
characteristics in online groups?
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39. Country-to-Country
Networks
• Immigration
• Political and Foreign
Relations
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40. Outline
• Background
• Social Media Use during the 2014 EuroMaidan Revolution
• Case of VKontakte
• Case of Twitter
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41. This part is based on…
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§ Gruzd, A., Mai P., and Kampen, A. (2017). A how-to for using
Netlytic to collect and analyze social media data: A case study of
the use of Twitter during the 2014 Euromaidan Revolution in
Ukraine.
In Sloan, L., & Quan-Haase, A. (Eds.). The SAGE Handbook of Social
Media Research Methods.

42. Source: Twitter Search API
Request Rate: Hourly, up to 1000 tweets
Time frame: Feb 18, 2014 – March 14, 2014
Tweeting about Ukraine in 3 languages
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Україна Украина Ukraine
Presumed
Language
Ukrainian Russian English
# Tweets 200,956 527,112 591,394
# Unique Users 46,641 141,541 246,113

43. http://www.theguardian.com/world/2014/feb/18/ukraine-police-
storm-kiev-protest-camp-live-updates#start-of-comments
Example: Twitter Communication Network about Ukraine
Tweets about
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44. @John
@Peter
@Paul • Nodes = People
• Ties = “Who retweeted/
replied/mentioned whom”
• Tie strength = The number of
retweets, replies or mentions
Communication Networks on Twitter
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45. Twitter Communication Network
# Tweets 200,956
# Unique
Users
46,641

46. Twitter Communication Network
Top 10
Mentioned
Users
(activists,
news,
politicians)
Jared Leto
expressed support
of Ukrainian people
in his Oscars award
acceptance speech
Jared Leto
expressed support
of Ukrainian people
in his Oscars award
acceptance speech

47. Twitter Communication Network
# Tweets 527,112
# Unique
Users
141,541

48. Twitter Communication Network
Top 10
Mentioned
Users
(mostly
Russian
news
agencies &
journalists)

49. # Tweets 591,394
# Unique
Users
246,113

50. Top 10
Mentioned
Users
(mostly
news
accounts)

55. Cross Network Comparison
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Україна (in Ukrainian) Украина (in Russian) Ukriane (in English)

56. Takeaways
• 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 groups and campaign.
• Need to perform a multi-platform analysis
• Need to know who and what types of networks we are
analyzing: Friendship vs Communication networks
• Geography Matters (even online)!
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57. Image credit: Geralt
Moving Forward
• Social media data as a
proxy to study online &
offline communities
• Combination of Social
Network Analysis (SNA)
and Spatiotemporal
Analysis
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58. STUDYING ONLINE & OFFLINE COMMUNITIES THROUGH
THE PRISM OF SOCIAL MEDIA DATA
ANATOLIYGRUZD (@GRUZD)
Canada Research Chair in Social Media Data Stewardship
Associate Professor, Ted Rogers School of Management
Director of the Social Media Lab
Ryerson University