Anatoliy Gruzd and Philip Mai Workshop presented at the TTRA Annual International Conference in Quebec City (June 20, 2017) https://2017ttraannualinternationalconfe.sched.com/event/9yCg/social-listening-how-to-do-it-and-how-to-use-it-veille-sociale-comment-faire-et-comment-lutiliser?iframe=no&w=100%&sidebar=no&bg=no

1. June 20, 2017
Philip Mai
@phmai
Director of Business &
Communications
Anatoliy Gruzd
@gruzd
Canada Research Chair
Associate Professor
Director of Research
@SMLabTO
Social Listening: How To Do It and How To Use It
Veille sociale: comment faire et comment l'utiliser
Social Media Lab
Ted Rogers School of Management,
Ryerson University

2. Outline
1. About the
Social Media
Lab
2. Social
Media Data
Collection
3. Intro to
SNA
4. Hands-on
Part
Slides at http://bit.ly/ttra-sna

3. We are an interdisciplinary academic research laboratory

4. ▪ Online Communities
▪ Social Network Analysis (SNA)
▪ Information Visualization & Dashboard
▪ Text Mining
▪ Distributed Computing
▪ Computer Mediated Communication
▪ Director of Research
▪ Director of Business & Communications
▪ Post Docs, Project Manager, PhD
Students, Master and Undergraduate
students
▪ Faculty Research Collaborators across
Canada and abroad (USA, UK, Russia,
Brazil, HK, S. Korea)
@SMLabTO
Our ExpertiseOur Team

5. At the Social Media LabWe Study…
How can social media
support online
communities, social
activism & political
engagement?
What does it mean to
be “influential”
online?
How can social media
help to enhance
teaching & learning
in higher education?
Is
happiness/swearing
contagious online?
What predicts user
engagement on
social media?
Is information privacy
dead in the social
media age?
@SMLabTOSocialMediaData.org
@SMLabTO

6. At the Social Media LabWe Organize…
International Conferences and Events
SocialMediaAndSociety.org AltmetricsConference.com
@SMLabTO

7. At the Social Media LabWe Develop…
Social Media Analytics Software
@SMLabTO

8. Anatoliy Gruzd
@SMLabTO
SOCIAL MEDIA RESEARCH TOOLKIT
socialmediadata.org
A Curated List of 50+
Peer Tested & Peer
Reviewed Social Media
Research Tools.
No Coding Required
(OK…maybe some
coding req.)

9. Outline
1. About the
Social Media
Lab
2. Social
Media Data
Collection
3. Intro to
SNA
4. Hands-on
Part
@SMLabTO
Slides at http://bit.ly/ttra-sna

10. Social Media sites have become
an integral part of our daily lives!
GROWTH OF SOCIAL MEDIA DATA
Facebook
1.8B
users
Instagram
600M
users
Twitter
300M
users
@SMLabTO

11. Decision Making
in domains such as Business, Education, Politics, Health Care, etc…
HOW TO MAKE SENSE OF SOCIAL MEDIA DATA?
Self-
collected
/reported
Public
APIs
Data
Resellers
@SMLabTO

12. HOW TO MAKE SENSE OF SOCIAL MEDIA DATA?
Big Data Technology
12
Cloud &
Distributed
Computing
Data &
Information
Organization
Analytics Visualization
@SMLabTO

13. Data -> Visualizations -> Understanding
HOW TO MAKE SENSE OF SOCIAL MEDIA DATA?
@SMLabTO

14. Nodes = People Edges /Ties (lines) = Relations
“Who retweeted/ replied/ mentioned whom”
HOW TO MAKE SENSE OF SOCIAL MEDIA DATA?
Social Network Analysis (SNA)
@SMLabTO

15. Makes it much easier to understand what is going on in a group
ADVANTAGES OF SOCIAL NETWORK ANALYSIS
Once the network is discovered, we can find out:
• How do people interact with each other?
• Who are influential users in a group?
• Who are susceptible to being influenced?
• Who is a human and who is a bot?
• …
Liberal
Conservative
Spam
Unknown &
Undecided
NDP
Left
Green
Bloc
Other
Gruzd, A. and Roy, J (2014). Political Polarization on Social
Media: Do Birds of a Feather Flock Together on Twitter?
Policy & Internet.
@SMLabTO

16. Common approach: surveys or interviews and self-reported social network
• A sample question about students’ perceived social structures
HOW DO WE COLLECT INFORMATION ABOUT SOCIAL NETWORKS?
Please indicate on a scale from [1] to [5],
YOUR FRIENDSHIP RELATIONSHIP WITH EACH STUDENT IN THE CLASS
[1] - don’t know this person
[2] - just another member of class
[3] - a slight friendship
[4] - a friend
[5] - a close friend
Alice D. [1] [2] [3] [4] [5]
…
Richard S. [1] [2] [3] [4] [5]
Source: C. Haythornthwaite, 1999
@SMLabTO

17. Problems with surveys or interviews
• Time-consuming
• Questions can be too sensitive
• Answers are subjective or incomplete
• Participant can forget people and
interactions
• Different people perceive events and
relationships differently
HOW DO WE COLLECT INFORMATION ABOUT SOCIAL NETWORKS?
@SMLabTO

18. Goal: Automated Networks Discovery
Challenge: Figuring out what content-based features of online interactions can
help to uncover nodes and ties between group members
HOW DO WE COLLECT INFORMATION ABOUT ONLINE SOCIAL NETWORKS?
@SMLabTO

19. Outline
1. About the
Social Media
Lab
2. Social
Media Data
Collection
3. Intro to
SNA
4. Hands-on
Part
Questions?
@SMLabTO
Slides at http://bit.ly/ttra-sna

20. @John
@Peter
@Paul • Nodes = People/Accounts
• Ties = “Who retweeted/
replied/mentioned whom”
• Tie strength = The number of
retweets, replies or mentions
AUTOMATED DISCOVERY OF SOCIAL NETWORKS
Twitter Networks
@SMLabTO

21. AUTOMATED DISCOVERY OF SOCIAL NETWORKS
Connection Discovery Examples
Network Ties
@Cheeflo -> @JoeProf
@Cheeflo -> @VMosco
@JoeProf -> @VMosco
Network Tie
@Gruzd -> @SidneyEve
Connection type: Mention
Connection type: Reply
@SMLabTO

22. SAMPLE TWITTER DATASET
SIMPLE SEARCH FOR #HONGKONG
3557 tweets @SMLabTO

23. #HONGKONG TWITTER NETWORK
What does this visualization
tell us?
3557 tweets
@SMLabTO

24. SNA MEASURES
Micro-level
In-degree centrality
Out-degree centrality
Betweenness centrality
Other centrality measures (e.g.,
closeness, eigenvector)
Macro-level
Density
Diameter
Reciprocity
Centralization
Modularity
@SMLabTO

25. SNA MEASURES
Micro-level
In-degree centrality
Out-degree centrality
Betweenness centrality
Other centrality measures (e.g.,
closeness, eigenvector)
 In-degree suggests “prestige”
highlighting the most mentioned
or replied Twitter users
@SMLabTO

26. IN-DEGREE CENTRALITY
#HONGKONG TWITTER NETWORK
Note: SEVENTEEN or SVT is
a S.Korean boy group formed
by Pledis Entertainment
@SMLabTO

27. SNA MEASURES
Micro-level
In-degree centrality
Out-degree centrality
Betweenness centrality
Other centrality measures (e.g.,
closeness, eigenvector)
 Out-degree reveals active
Twitter users with a good
awareness of others in the
network
@SMLabTO

28. OUT-DEGREE CENTRALITY
#HONGKONG TWITTER NETWORK
Note: A music fan (many
retweets & replies to others)
@SMLabTO

29. SNA MEASURES
Micro-level
In-degree centrality
Out-degree centrality
Betweenness centrality
Other centrality measures (e.g.,
closeness, eigenvector)
Anatoliy Gruzd
 Betweenness shows actors who
are located on the most number
of information paths and who
often connect different groups of
users in the network
Twitter: @gruzd
@SMLabTO

30. BETWEENNESS CENTRALITY
#HONGKONG TWITTER NETWORK
Note: A fan (retweets/replies to
messages from two different fan
communities/sites)
@SMLabTO

31. Outline
1. About the
Social Media
Lab
2. Social
Media Data
Collection
3. Intro to
SNA
4. Hands-on
Part
Questions?
@SMLabTO
Slides at http://bit.ly/ttra-sna

32. Case Study: #ExploreCanada on Twitter by
Destination Canada
Twitter: @gruzd ANATOLIY GRUZD 34

33. Practice with Netlytic.org
Exploring #ExploreCanada via Social Network Analysis
Tutorial Steps:
https://netlytic.org/home/?p=11510
Also see
Gruzd, A., Paulin, D., & Haythornthwaite, C. (2016).
Analyzing Social Media and Learning Through Content
and Social Network Analysis: A Faceted Methodological
Approach. Journal of Learning Analytics 3(3). Available
at https://eric.ed.gov/?id=EJ1126777
Twitter: @gruzd ANATOLIY GRUZD 35

34. Twitter: @gruzd ANATOLIY GRUZD 36
Live Demo: https://netlytic.org/network/sigma.php?c=YIZ5D21C5m05K2FV&viz=2&datatype=twitter

35. SNA Measures
Micro-level
In-degree centrality
Out-degree centrality
Betweenness centrality
Other centrality measures (e.g.,
closeness, eigenvector)
Macro-level
Density
Diameter
Reciprocity
Centralization
Modularity
ANATOLIY GRUZD 37@gruzd

36. Sample Twitter Searches
#ELECTION2016 #HONGKONG
Twitter: @gruzd ANATOLIY GRUZD 38
3557 records1394 records

37. Sample Twitter Searches
#ELECTION2016 #HONGKONG
Twitter: @gruzd ANATOLIY GRUZD 39
3557 records1394 records

38. SNA Measures
Macro-level
Density
Diameter
Reciprocity
Centralization
Modularity
Density indicates the overall
connectivity in the network (the total
number of connections divided by the
total number of possible connections).
It is equal to 1 when everyone is
connected to everyone.
ANATOLIY GRUZD 40Twitter: @gruzd
User1 User3
User2
Density = 1

39. #Election2016 #HongKong
Nodes 491 2570
Edges 1075 2447
Density 0.005 (0.5%) 0.0004 (0.04%)
Diameter
Reciprocity
Centralization
Modularity
ANATOLIY GRUZD 41Twitter: @gruzd

40. SNA Measures
Macro-level
Density
Diameter
Reciprocity
Centralization
Modularity
Diameter gives a general idea of how
“wide” the network is; the longest of the
shortest paths between any two nodes in
the network.
ANATOLIY GRUZD 42Twitter: @gruzd
#1
User1
User3
User2
User4
Diameter = 3
#2
#3

41. #Election2016 #HongKong
Nodes 491 2570
Edges 1075 2447
Density 0.005 (0.5%) 0.0004 (0.04%)
Diameter 28 14
Reciprocity
Centralization
Modularity
ANATOLIY GRUZD 43Twitter: @gruzd

42. SNA Measures
Macro-level
Density
Diameter
Reciprocity
Centralization
Modularity
Reciprocity shows how many online
participants are having two-way
conversations.
In a scenario when everyone replies to
everyone, the reciprocity value will be 1.
ANATOLIY GRUZD 44Twitter: @gruzd
User2
User1
User3
User4 Reciprocity=1

43. #Election2016 #HongKong
Nodes 491 2570
Edges 1075 2447
Density 0.005 (0.5%) 0.0004 (0.04%)
Diameter 28 14
Reciprocity 0.006 (0.6%) 0.003 (0.3%)
Centralization
Modularity
ANATOLIY GRUZD 45Twitter: @gruzd

44. SNA Measures
Macro-level
Density
Diameter
Reciprocity
Centralization
Modularity
Centralization indicates whether a network is
dominated by few central participants
(values are closer to 1),
or whether more people are contributing to
discussion and information dissemination
(values are closer to 0).
ANATOLIY GRUZD 46Twitter: @gruzd
User2
User1User3
User4 Centralization=1

45. #Election2016 #HongKong
Nodes 491 2570
Edges 1075 2447
Density 0.005 (0.5%) 0.0004 (0.04%)
Diameter 28 14
Reciprocity 0.006 (0.6%) 0.003 (0.3%)
Centralization 0.05 0.11
Modularity
ANATOLIY GRUZD 47Twitter: @gruzd

46. SNA Measures
Macro-level
Density
Diameter
Reciprocity
Centralization
Modularity
Modularity provides an estimate of
whether a network consists of one
coherent group of participants who are
engaged in the same conversation and
who are paying attention to each other
(values closer to 0);
or whether a network consists of
different conversations and
communities with a weak overlap
(values closer to 1).
ANATOLIY GRUZD 48Twitter: @gruzd

47. #Election2016 #HongKong
Nodes 491 2570
Edges 1075 2447
Density 0.005 (0.5%) 0.0004 (0.04%)
Diameter 28 14
Reciprocity 0.006 (0.6%) 0.003 (0.3%)
Centralization 0.05 0.11
Modularity 0.42 0.92
ANATOLIY GRUZD 49Twitter: @gruzd

48. Twitter: @gruzd ANATOLIY GRUZD 50
Live Demo: https://netlytic.org/network/sigma.php?c=YIZ5D21C5m05K2FV&viz=2&datatype=twitter
Hands-On Part
Macro-level
SNA Measures
in Netlytic

49. #ExploreCanada #Election2016 #HongKong
Nodes 1196 491 2570
Edges 2017 1075 2447
Density 0.0014 (0.14%) 0.005 (0.5%) 0.0004 (0.04%)
Diameter 18 28 14
Reciprocity 0.0188 (1.9%) 0.006 (0.6%) 0.003 (0.3%)
Centralization 0.13 0.05 0.11
Modularity 0.70 0.42 0.92
ANATOLIY GRUZD 51@gruzd

50. Outline
1. About the
Social Media
Lab
2. Social
Media Data
Collection
3. Intro to
SNA
4. Hands-on
Part
@SMLabTO
Slides at http://bit.ly/ttra-sna

51. June 20, 2017
Philip Mai
@phmai
Director of Business &
Communications
Anatoliy Gruzd
@gruzd
Canada Research Chair
Associate Professor
Director of Research
@SMLabTO
Social Listening: How To Do It and How To Use It
Veille sociale: comment faire et comment l'utiliser
Social Media Lab
Ted Rogers School of Management,
Ryerson University