A brief 10 minute lecture on recognising and explaining user patterns in social media data using social network analytics (SNA).

1. SOCIAL NETWORK ANALYSIS :
RECOGNISING AND EXPLAINING
PATTERNS IN SOCIAL DATA
Social Media Analytics
Dr.Joseph Asamoah

2. Lesson Recap
Lesson 1.
THEORIES
1. Social Identity Theory
2. Social Capital Theory
3. Social Network Theory

3. Social Networks
◦ Social Networks are applications that augment group interactions ,
collaborations , social connections and information exchanges (Boskir and Sezer,
2010).
◦ Notable online social networks comprise Facebook, Twitter, Instagram, Snap
Chat , LinkedIn and Tik Tok.
◦ Most marketers and social researchers are interested in understanding online
social relationships.

4. What is Social Network Analysis?
Social network analysis is
the premise that social life
is created by relations
and the patterns formed.
Social networks are
formally defined as a set
of nodes (or network
members) that are tied by
one or more types of
relations.

5. Relationships
1. Where one person is
connected to another person.
2. Nodes (“Actor” ) or people
in the relationship
3. Edge (relationship
connecting nodes)
4. Clustering or community

6. Key Metrics
Helps to identify the most
critical nodes in a graph (i.e
most influential or popular
person in a network) –
Number of direct
connections that
individuals have with
others in the group
(Degree Centrality).

7. Key Metrics
Betweenness
centrality measures the number
of times a node lies on the
shortest path between other
nodes.
For finding the individuals
who influence the flow around
a system and have a powerful
position in the network.

8. Importance for Digital Marketers
◦ Social Networks can use SNA to promote placements of marketing (Placement
Ads Targeting).
◦ Consumer brands are using SNA to identify influencers (Influencer
Identification).
◦ Consumer brands can craft content that could be more valuable for the audience
(Content Targeting).

9. Case Study (Black Mirror - Nosedive)

10. Lesson Recap
◦ 1. What is the “edge(s)” or “connecting relationship”?
◦ 2. What nodes or actors will be the most influential in the
relationship?
◦ 3. What segment or clusters can be deduced from the
relationship?

11. Next Lesson
Lesson 3
DATA MINING AND SOCIAL MEDIA
1. Descriptive and Predictive techniques
2. Applications to Internet Marketing
3. Ethics and Privacy

12. Further Reading
◦ Predictive Analytics & Facebook – A Love Story
https://www.predictiveanalyticsworld.com/machinelearningtimes/predictive-analytics-facebook-
a-love-story0823151/6117/
◦ Hair, J.F (2007), "Knowledge creation in marketing: the role of predictive analytics", European
Business Review, Vol. 19 No. 4, pp. 303-315. https://doi.org/10.1108/09555340710760134
◦ Raja, B., Pamina, J., Madhavan, P. and Kumar, A., 2019. Market Behavior Analysis using
Descriptive Approach. SSRN Electronic Journal,. https://acadpubl.eu/jsi/2018-118-7-
9/articles/7/23.pdf
◦ What is the Cambridge Analytics Scandal? (2018)
https://www.youtube.com/watch?v=Q91nvbJSmS4

13. References
◦ Bozkir, Ahmet & Sezer, Ebru. (2010). Identification of User Patterns in Social Networks by Data Mining
Techniques: Facebook Case. Communications in Computer and Information Science. 96. 145-153.
10.1007/978-3-642-16032-5_13.
◦ Akhtar, Nadeem & Javed, Hira & Sengar, Geetanjali. (2013). Analysis of Facebook Social Network. 451-454.
10.1109/CICN.2013.99.
◦ Hoffman, M., Steinley, D., Gates, K. M., Prinstein, M. J., & Brusco, M. J. (2018). Detecting
Clusters/Communities in Social Networks. Multivariate behavioral research, 53(1), 57–73.
https://doi.org/10.1080/00273171.2017.1391682
◦ Zhang, B., 2013. Social Network Analysis On Digital Marketing. [online] YouTube. Available at:
<https://www.youtube.com/watch?v=Zo6gNqyJnMw> [Accessed 23 September 2020].

14. QUESTION TIME