The document discusses using social network analysis to identify influencers on social media. It describes the Social Media Lab and its use of the Netlytic software to analyze social networks and identify influential users. As a case study, the document analyzes Twitter data about Ryerson University to identify different types of influencers in the conversation, concluding that the influential users depend on the metrics and networks examined.

1. Identifying Influencers on Social Media Using
Social Network Analysis
Felipe Bonow Soares
PhD candidate in Communication and Information (UFRGS)
Visiting researcher (Social Media Lab)
@felipebsoares
@SMLabTO
1

2. 2

3. 3

4. Agenda
• Social Media Lab
• Social Network Analysis (SNA)
• Software: Netlytic (Netlytic.org)
• Case Study: @RyersonU
@felipebsoares
@SMLabTO 4

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Co-Directors
The Lab & The People “Making Sense of a Networked World”
The lab’s research seeks to
advance the public’s understanding
of the benefits and pitfalls of social
media adoption.
@felipebsoares
@SMLabTO 5

6. Allan
Mattsson
CEO
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Mira
Karlsson
COO
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Victoria
Lindqvist
CTO
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8-12 Students ea. year
90+ since 2010
Thesis Students, Research
Assistants, Postdocs (HQPs)
Socialmedialab.ca
@felipebsoares
@SMLabTO 6

7. Research Collaborators
Allan
Mattsson
CEO
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Mira
Karlsson
COO
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adipiscing elit
Victoria
Lindqvist
CTO
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North America, Europe,
Asia & South America
Currently:
16+ active collaborators
Socialmedialab.ca
@felipebsoares
@SMLabTO 7

8. Visiting Scholars
Unique
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First to Market
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Tested
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Authentic
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USA
Europe
South
America
Asia
Canada
2016-2019
13 Visiting Scholars
@felipebsoares
@SMLabTO 8Socialmedialab.ca

9. Funding Partners
Productivity
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User Frustration
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Mobile Finance
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Chip Pricing
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Scarce Analytics
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Current and Past 2.25 M+ since 2010
1.25 M+ since joining TRSM in 2014
9
@felipebsoares
@SMLabTO Socialmedialab.ca

10. Research Foci
Social Media for:
Research,
Teaching,
Learning, Learning
Analytics & Alt-
metrics
Health, Well-being
and Social Media
Social Media Data
Use, Reuse &
Misuse, Privacy &
Trust, Privacy
Paradox
Online Social
Networks, Online
Crowds &
Communities
Adoption & Usage
Trends, Factors
Predicting
Engagement, ROI,
Data Analytics &
Visualizations
Political
Engagement &
Polarization, Social
Movements, & E-
Government
The Spread of Dis-
and Misinformation
via Social Media
10
@felipebsoares
@SMLabTO Socialmedialab.ca

11. Research Contributions
2014-2019
71 High Impact Publications [IF 1.07-5.23]
Avg. 14 Research Outputs/Year
21
1611
9
6
5
3
Posters
Journal
articlesConference
abstracts
Conference
papers
Public
reports
Book
chapters
Ed. Journal
issues
2014-2019
11Socialmedialab.ca
@felipebsoares
@SMLabTO

12. Public Reports
Social Media Adoption & Usage Trends
Data About Canadians For Canadians
12Socialmedialab.ca
@felipebsoares
@SMLabTO

13. Earned Media
2014-2019
98 Media Mentions
Avg. 20/Year
The
Washington
Post
MIT
Technology
Review
Foreign
Affairs
CBS News The Atlantic Nature
NPR
Los Angeles
Times
The Globe
and Mail
CBC Radio
‘Spark’
TVO The
Agenda
Maclean’s
13Socialmedialab.ca
@felipebsoares
@SMLabTO

14. Research Tools & Apps
Data Analytics and Data Visualizations Software
Socialmedialab.ca/apps/
14
@felipebsoares
@SMLabTO

15. Scholarly Events
“ ”
2016
2017
2018
2019
2010
400+ Authors,
200+ Attendees from
25+ Countries each year
10 Years Running
2020
15
@felipebsoares
@SMLabTO Socialmedialab.ca

16. Agenda
• Social Media Lab
• Social Network Analysis (SNA)
• Software: Netlytic (Netlytic.org)
• Case Study: @RyersonU
@felipebsoares
@SMLabTO 16

17. @felipebsoares
@SMLabTO
How to make sense of social media data?
17

18. How to make sense of social media data?
Method: Social Network Analysis (SNA)
18
@felipebsoares
@SMLabTO

19. Nodes and edges
Nodes: social actors
19
A
B
C
D
E
@felipebsoares
@SMLabTO

20. Nodes and edges
Nodes: social actors Edges: connections
20
A
B
C
D
E
@felipebsoares
@SMLabTO

21. Nodes and edges
Undirected Network
Unweighted (Binary)
21
@felipebsoares
@SMLabTO

22. Nodes and edges
Directed Network
Weighted (Valued)
Undirected Network
Unweighted (Binary)
1@/RT
3@/RT
22
@felipebsoares
@SMLabTO

23. What do we study in networks?
23
@felipebsoares
@SMLabTO

24. Influencers: Centralities
Source: https://friendsarena.wordpress.com/category/uncategorized/
24
@felipebsoares
@SMLabTO

25. Influencers: Centralities
Source: https://friendsarena.wordpress.com/category/uncategorized/
25
@felipebsoares
@SMLabTO

26. In-degree and Out-degree
26
@felipebsoares
@SMLabTO

27. In-degree and Out-degree
27
@felipebsoares
@SMLabTO

28. Political Polarization, US political blogs, 2004 elections
(Blue: democrats, Red: republicans)
Source: Adamic and Glance (2005)
28
@felipebsoares
@SMLabTO
Networks are everywhere:
Political Networks

29. Networks are everywhere:
Political Networks
Political Polarization, US Congress
(Blue: democrats, Red: republicans)
Source: Andris et al. (2015)
29
@felipebsoares
@SMLabTO

30. Networks are everywhere:
Political Networks
Political Polarization on Twitter, Canadian Election
Source: Gruzd et al. (2014)
30
@felipebsoares
@SMLabTO

31. Networks are everywhere:
Network of Thrones
from Games of Thrones
31
@felipebsoares
@SMLabTO
Source: Beveridge & Shan (2016)

32. 32
Networks are everywhere:
Ingredients network
@felipebsoares
@SMLabTO
Source : Teng, Lin & Adamic (2012)

33. Networks are everywhere:
Sports
33
@felipebsoares
@SMLabTO Source: https://twitter.com/BetweenThePosts

34. • Reduce the large quantity of
data into a more concise
representation
• Makes it much easier to
understand what is going on
in a group
• 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
• Much more!
34
How to Make Sense of Social Media Data?
Advantages of Social Network Analysis (SNA)
@felipebsoares
@SMLabTO

35. Agenda
• Social Media Lab
• Social Network Analysis (SNA)
• Software: Netlytic (Netlytic.org)
• Case Study: @RyersonU
35
@felipebsoares
@SMLabTO

36. • A social media data collector
• A text and social networks analyzer
• Designed to analyze public conversational data
and visualize social networks
• Used by thousands of students and
researchers for learning, teaching and research
NETLYTIC
A SOCIAL MEDIA ANALYTIC PLATFORM
@felipebsoares
@SMLabTO 36

37. Netlytic
www.netlytic.org
Visualize & analyze social networks
Discover popular topicsCollect data from social media
Find & explore emerging
themes of discussions 37
@felipebsoares
@SMLabTO

38. Agenda
• Social Media Lab
• Social Network Analysis (SNA)
• Software: Netlytic (Netlytic.org)
• Case Study: @RyersonU
38
@felipebsoares
@SMLabTO

39. 39
@felipebsoares
@SMLabTO
Case study: @RyersonU
Question: Who are the influencers in the
conversation about Ryerson University?

40. 40
@felipebsoares
@SMLabTO
Case study: @RyersonU
Question: Who are the influencers in the
conversation about Ryerson University?

41. 41
@felipebsoares
@SMLabTO
Case study: @RyersonU
Question: Who are the influencers in the
conversation about Ryerson University?
Jan 7 - Jan 15, 2020
978 tweets
675 users
Text analysis
SNA

42. Case study: @RyersonU
42
@felipebsoares
@SMLabTO
Text analysis

43. Case study: @RyersonU
43
@felipebsoares
@SMLabTO
Text analysis

44. Case study: @RyersonU
44
@felipebsoares
@SMLabTO
Text analysis

45. Case study: @RyersonU
45
@felipebsoares
@SMLabTO
Text analysis

46. Automated Discovery of Social Networks:
Twitter Data Examples
Network Edge
@felipebsoares -> @TBallinhas
Connection type: Reply (Chain Network)
@felipebsoares
@SMLabTO 46

47. Automated Discovery of Social Networks:
Twitter Data Examples
Network Edge
@felipebsoares -> @SMLabTO
Network Edge
@felipebsoares -> @TBallinhas
Connection type: Mention/RT (Name Network)
Connection type: Reply (Chain Network)
@felipebsoares
@SMLabTO 47

48. Case study: @RyersonU
48
@felipebsoares
@SMLabTO

49. Case study: @RyersonU
49
@felipebsoares
@SMLabTO

50. Case study: @RyersonU
50
@felipebsoares
@SMLabTO

51. Case study: @RyersonU
51
@felipebsoares
@SMLabTO

52. Case study: @RyersonU
52
@felipebsoares
@SMLabTO

53. Case study: @RyersonU
53
@felipebsoares
@SMLabTO

54. Who are the influencers in the
conversation about Ryerson University?
54
@felipebsoares
@SMLabTO

55. Who are the influencers in the
conversation about Ryerson University?
Answer: It depends!
55
@felipebsoares
@SMLabTO

56. Who are the influencers in the
conversation about Ryerson University?
Answer: It depends!
56
@felipebsoares
@SMLabTO

57. Summary:
• SNA helps us understand social interactions
• We can use Netlytic to collect and analyze social media data
• SNA is useful to identify social groups on social media
• SNA can be used to identify influencers on social media
• There are different types of influencers on social media
• SNA is also useful to answer many other questions related to
social media interactions
57
@felipebsoares
@SMLabTO

58. Thank you!
Felipe Bonow Soares
PhD candidate in Communication and Information
Visiting researcher at the Social Media Lab
felipeb.soares@ryerson.ca
@felipebsoares
58
@felipebsoares
@SMLabTO