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Social Media Analytics Lecture
1. Social Media Analytics
Wasim Ahmed
Email: wahmed1@Sheffield.ac.uk
Guest Lecture for INF6032 Big Data Analytics
Monday 24th April 2017
2. About me
• Third Year PhD student in the Health Informatics Research
Group, Information School, University of Sheffield. (Faculty
Scholarship).
• CEO of Sonic Social Media - advise and work with a number
of social media monitoring and analytics organisations as
well as multi-million turnover brands.
• Run an analytics blog with readership in over 196 countries.
Read across media, government, and academia.
12. • Most frequently shared URLs, Domains, Hashtags,
Words, Word Pairs, Replied-To, Mentioned Users, and
most Frequent Tweeters.
• Produces analytics overall and by group of users (users
are grouped by tweet content).
• By looking at different metrics associated with different
groups (G1, G2, G3 etc) you can see the different topics
that users may be talking about.
NodeXL Produces a Number of Analytics
13. Centrality
• NodeXL also produces centrality measures
– Centrality measures help address the question:
who is the most important or central person in
this network?
– Centrality measures include:
• Degree centrality
• Closeness centrality
• Betweenness centrality
• Eigenvector centrality
• PageRank centrality
14. Betweenness Centrality
From Richard Ingram’s blog post visualising
Data: Seeing is Believing
http://www.richardingram.co.uk/2012/12/visu
alising-data-seeing-is-believing/
15. Degree Centrality
From Richard Ingram’s blog post visualising
Data: Seeing is Believing
http://www.richardingram.co.uk/2012/12/visu
alising-data-seeing-is-believing/
38. World Economic Forum
• They weren’t engaging journalists and wanted to see
their events gain coverage.
• Used Audiense to launch direct message campaigns to
key segmented users.
• Led to coverage in the BBC, Bloomberg, CNN, and
many more outlets.
38
https://audiense-blog.s3.amazonaws.com/case-
studies/World%20Economic%20Forum%20-
%20Audiense%20Case%20Study%202016.pdf