Social Network Analysis - Inforte course on Big Social Data Analytics, Tampere, 2017

1. Social Network Analysis
Inforte course on Big Social Data Analytics 2017
Dr. Jari Jussila
Twitter: @jjussila
Email: jari.j.jussila@tut.fi
GitHub: https://github.com/jjussila/BigSocialDataAnalytics

2. WEB
MOBILE AND SOCIAL MEDIA
ERP
CRM
Purchase &
Transaction
Records
Offers and
Quotations
Customer
Engagements
A/B Testing
Dynamic Pricing
Search Engine
Marketing and
Optimization
Target Marketing
Images and Videos
Speech to Text
Sensor Data
Application Log Data
SMS/MMS
Location Data
Social Network Analysis
From transactions to interactions
Social Media Posts
Customer
Segmenting

3. Network Analysis (NA) &
Social Network Analysis (SNA)

4. Graph and Matrix
Representation of Networks
Star
Circle
Chain
0 1 1 1 1 1 1
1 0 0 0 0 0 0
1 0 0 0 0 0 0
1 0 0 0 0 0 0
1 0 0 0 0 0 0
1 0 0 0 0 0 0
0 1 0 0 0 0 1
1 0 1 0 0 0 0
0 1 0 1 0 0 0
0 0 0 1 0 1 0
0 0 0 0 1 0 1
1 0 0 0 0 1 0
0 1 1 0 0 0 0
1 0 0 1 0 0 0
1 0 0 0 1 0 0
0 1 0 0 0 1 0
0 0 1 0 0 0 1
0 0 0 1 0 0 0
0 0 0 0 1 0 0
Matrix

5. Directed and Undirected
Networks
B
A
C
A B C
A 0 0 1
B 1 0 0
C 0 1 0
B
A
C
A B C
A 0 1 1
B 1 0 1
C 1 1 0

6. Sociomatrix
Jim Bob Alex Tom
Jim - 0 1 0
Bob 1 - 1 1
Alex 1 1 - 1
Tom 0 1 1 -
Relationship: is friend of
Source: Hoffman 2000; Moreno 1953
“the mathematical study of psychological properties of populations, the
experimental technique of and the results obtained by application of quantitative
methods” (Moreno, 1953, pp. 15-16).

7. Direct and Indirect Paths
(Friends/Connections/etc.)

8. Nodes and Edges
GephiNodeXL

9. Anatomy of Networks

10. Network Metrics:
Prominence
Centrality Prestige
Prominence
Degree
Centrality
Closeness
Centrality
Degree
Prestige
Proximity
Prestige
Betweeness
Centrality
Information
Centrality
Status or Rank
Prestige
Source: Wasserman & Faust 1994

11. • Degree
• How many direct links a node has to other nodes
• In the case of a directed network it is possible to
calculate both indegree (incoming connections)
and outdegree (outgoing connections)
11
Degree Centrality
Source: Wasserman & Faust 1994

12. • Closeness is the sum of shortest paths of a node to
other nodes in the network
• dij length of shortest path between i and j
• Closeness centrality indicates how quickly a node can
interact with other nodes
å=
=
n
ij
iji dc
Closeness Centrality
Source: Wasserman & Faust 1994

13. • Betweennes measures the degree to which a node is
located at the shortest paths between two nodes
• Betweennes centrality indicates the ability of node to
control information between other nodes (gatekeeper)
• A node may not be locally central, but may still have a
high betweenness centrality
13
Betweenness Centrality
Source: Wasserman & Faust 1994

14. Network Analysis Process in
Practice
• Network Analysis process usually consists of
the following four phases:
1. Interpreting the phenomena under
investigation as a network
2. Collecting data
3. Cleaning and refining the data
4. Network layout and fine-tuning
Source: Huhtamäki & Parviainen 2015

15. A process for visualization
Source: Card et al. 1999

16. Visualization Stages
Visual and
Cognitive
Processing
Physical Environment
Social Environment
Data gathering
Data
Preprocessing
and
transformation
Visualization
Tool
Data manipulation
Data exploration
Source: Ware 2004

17. OSTINATO Process Model for
Visual Network Analysis
Source: Huhtamäki 2016

18. Entity Recognition?
• Twitter provides natural identifiers for nodes
(however some nodes maybe fake accounts or
bots)
• In some other application areas, such as,
bibliographic data analysis entity recognition is
more problematic
• Entity Recognition can be done in network
visualization tools (e.g. Gephi Data Laboratory)
or using third-party applications (e.g. Open
Refine)

19. Entity Recognition in Gephi Data
Laboratory
22.5.2017 19
Source Target

20. Node and Edge Creation
DiGraph – Directed graphs with self loops
Each user mention creates an edge between users. For Twitter Mentions see:
https://support.twitter.com/articles/14023#

21. Visual Properties Configuration
Node Partition by Modularity Class

22. Layout Processing: Force-driven
layout
• Layout refers to the act of placing the nodes on
canvas
• Force-driven layout is a straightforward option:
– Nodes repel each other
– Connections act as springs pulling the nodes back
together
– The center of a gravitational field is placed in the
middle of the canvas
– The process is run and configured in iteration until the
visualizer is happy with the result
Source: Huhtamäki 2015

23. Example
Source: Huhtamäki et al. 2012
The list of startups participating
in the Tekes YIC program was
scraped from Tekes homepage.
The IEN Dataset was used to
gather data on companies,
investors, key individuals, and
acquisitions.
Moreover, the Twitter
usernames of the YIC
companies were compiled in a
spreadsheet in a semi-manual
manner, and a tailored script
was implemented to crawl
Twitter REST API to collect the
list of followers of each YIC
company with a Twitter
account.

24. Interactive Network Visualization
Source: Aramo-Immonen et al. 2016; Aramo-Immonen et al. 2015
http://www.tut.fi/novi/case/2015-cbh-cmadfi2014-informallearning/twomode/network/

25. Hashtag Co-Occurrence Matrix
http://www.tut.fi/novi/case/2015-cbh-cmadfi2014-informallearning/hashtags/matrix/
Source: Aramo-Immonen et al. 2016; Aramo-Immonen et al. 2015

26. Extraction of Twitter data
and Network Visualization
with Gephi

27. Steps
• Collect the Twitter data
– Download the following script for extracting tweets:
https://github.com/jjussila/BigSocialDataAnalytics/blob/master/sc
ripts/search_trump.py
– Create a Twitter account or borrow from friend, if you do not
already have one
– Create a Twitter App https://apps.twitter.com/
– Create keychain.json file (that includes necessary keys and
tokes for accessing the data)
• Start running Python code online
– https://www.pythonanywhere.com/
• Install the following software
– Gephi https://gephi.org/ (for network visualization)

28. Original Twitter-api script
Source: https://github.com/jukkahuhtamaki/pcm-demo/tree/master/twitter-api

29. Modified script of extracting
Twitter đata
Source: https://github.com/jjussila/BigSocialDataAnalytics

30. Become a Twitter Developer

31. Create your first Twitter App

32. Get the keys and tokens needed to
access Twitter data

33. Create keychain.json using
template file
Copy-paste from Twitter App the necessary keys and tokens
and save the file as keychain.json

34. Example of extracting tweet data

35. Modifying the script
Note:
%40 = ‘@’
%23 = ‘#’’
For more details see:
w3schools.com
ASCII Encoding Reference

36. Network creation with NetworkX
library
Source: NetworkX

37. Using PythonAnywhere
Upload the following files:
- search_twitter.py
- keychain.json

38. Running Python code on
PythonAnywhere
Start a new console:
Bash

39. Execute Python script in Bash
console
22.5.2017 39

40. Using PythonAnywhere
Download the following files:
- network.gexf

41. Open gexf (Graph Exchange XML Format)
with Gephi

42. Calculate the Network Metrics and
Visualize the Network
Modularity Report
(Community Detection Algorithm)

43. References
• Aramo-Immonen, H., Kärkkäinen, H., Jussila, J. J., Joel-Edgar, S., & Huhtamäki, J. (2016).
Visualizing informal learning behavior from conference participants' Twitter data with the Ostinato
Model. Computers in Human Behavior, 55, 584-595.
• Aramo-Immonen, H., Jussila, J., & Huhtamäki, J. (2015). Exploring co-learning behavior of
conference participants with visual network analysis of Twitter data. Computers in Human
Behavior, 51, 1154-1162.
• Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring
and manipulating networks. ICWSM, 8, 361-362.
• Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization:
using vision to think. Morgan Kaufmann.
• Huhtamäki, J. (2016). Ostinato Process Model for Visual Network Analytics: Experiments in
Innovation Ecosystems. (Tampere University of Technology. Publication; Vol. 1425). Tampere
University of Technology.
• Huhtamäki, J., Still, K., Isomursu, M., Russell, M., & Rubens, N. (2012, September). Networks of
Growth: The Case of Young Innovative Companies in Finland. In Proceedings of the 7th European
Conference on Innovation and Entrepreneurship: ECIE (p. 307). Academic Conferences Limited.
• Huhtamäki, J., & Parviainen, O. (2013). Verkostoanalyysi sosiaalisen median tutkimuksessa.
Otteita verkosta-Verkon ja sosiaalisen median tutkimusmenetelmät. Vastapaino, Tampere.
• Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014). ForceAtlas2, a continuous graph
layout algorithm for handy network visualization designed for the Gephi software. PloS one, 9(6),
e98679.
• McSweeney, P. J. (2009). Gephi Network Statistics. Presentado en Google Summer of Code.
Recuperado a partir de http://gephi. org/google-soc/gephi-netalgo. pdf.
• Ware, C. (2013). Information visualization: perception for design (Third ed.): Elsevier.
• Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8).
Cambridge university press.