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Social Media Analytics
Wasim Ahmed
Email: wahmed1@Sheffield.ac.uk
Guest Lecture for INF6032 Big Data Analytics
Monday 24th April 2017
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.
24/04/2017 © The University of Sheffield
3
https://wasimahmed.org/about/
http://blogs.lse.ac.uk/impactofsocialsciences/?s=wasim+ahmed
Published a number of
research papers, and
blogged widely.
24/04/2017 © The University of Sheffield
4
• Delivered a number of talks related to my research such
as to the government , media , and industry.
• Upcoming talk to delegates at the European Centre for
Nuclear Research at CERN in Geneva. June, 2017.
• Co-running a Summer School in Šibenik, Croatia on
social media analytics. June 2017.
Recent and Upcoming talks
Lecture Aims
• Develop knowledge on the types of social media
analytics that are possible.
• Gain an overview of social media analytics tools.
• Understand how social media analytics have
been put to use by organisations.
24/04/2017 © The University of Sheffield
5
24/04/2017 © The University of Sheffield
6
• Twitter has over 313 million monthly active users1 –
consumers can use this channel to express their views.
• Businesses spend millions every year tailoring their
brands and protecting them.
• Brands can use Twitter to tap into and target consumers
and may spend a lot of money in doing so.
1 https://about.twitter.com/company
Twitter
24/04/2017 © The University of Sheffield
7
• Open API so anyone with an Internet connection can
retrieve data
• Open platform where anyone can follow anyone and can
request to follow other users
• A lot of meta-data fields available to developers to
create analytics apps
Why is Twitter so popular?
24/04/2017 © The University of Sheffield
8
United Airlines
• Recent example of a PR
disaster
• To-date this has received
n=170,346 retweets and
n=151,014 likes
24/04/2017 © The University of Sheffield
9
Immediate Twitter Aftermath of United Airlines -
NodeXL Network Graph
24/04/2017 © The University of Sheffield
10
American Airlines
24/04/2017 © The University of Sheffield
11
American Airlines (Heat Map using
TrendsMap)
• 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
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
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/
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/
[Divided]
Polarized Crowds
[Unified]
Tight Crowd
[Fragmented]
Brand Clusters
[Clustered]
Community Clusters
[In-Hub & Spoke]
Broadcast Network
[Out-Hub & Spoke]
Support Network
6 kinds of Twitter networks
[Divided]
Polarized Crowds
[Unified]
Tight Crowd
[Fragmented]
Brand Clusters
[Clustered]
Community Clusters
[In-Hub & Spoke]
Broadcast Network
[Out-Hub & Spoke]
Support Network
6 kinds of Twitter networks
24/04/2017 © The University of Sheffield
18
How can you use this?
• You can use social network analysis to
identify influencers and people who are
interested in a particular topic.
• You can identify clusters of users interested in
a particular topic and use automated methods
to target them.
#WorldMentalHealthDay
24/04/2017 © The University of Sheffield
20
In-built Twitter Analytics
24/04/2017 © The University of Sheffield
21
Software As a Service (SAS)
• A number of online tools have emerged
utilising Twitter data and generated good
revenue by selling analytics as a service.
• Services such as:
• Brand and Media Monitoring
• Consumer Engagement
• Security
24/04/2017 © The University of Sheffield
22
Visibrain
Audiense
TrendsMap
Echosec
DiscoverText
NodeXL
24/04/2017 © The University of Sheffield
23
IBM Watson Personality Insights
https://www.ibm.com/watson/developercloud/pe
rsonality-insights.html#how-it-works-block
24/04/2017 © The University of Sheffield
24
Twitter as a Media Monitoring Tool
24/04/2017 © The University of Sheffield
25
Monitoring Keywords for Brand Monitoring
24/04/2017 © The University of Sheffield
26
Monitoring Keywords for Brand Monitoring
24/04/2017 © The University of Sheffield
27
Social Media For Security – Monitoring Locations
24/04/2017 © The University of Sheffield
28
Social Media For Security – Monitoring Locations
24/04/2017 © The University of Sheffield
29
Social Media Analytics For Research (DiscoverText)
Over 150 mentions of DiscoverText in academic research
24/04/2017 © The University of Sheffield
30
DiscoverText – Powerful Filtering
24/04/2017 © The University of Sheffield
31
Social Media Analytics for Consumer Engagement (Audiense)
24/04/2017 © The University of Sheffield
32
Social Media Analytics for Consumer Engagement
(Audiense)
• You can leverage the back end analytics
provided by Twitter to build specific
audiences.
• You can use this information target users and
monitor the performance of the message that
you send.
24/04/2017 © The University of Sheffield
33
Filter and Target
24/04/2017 © The University of Sheffield
34
Using R for Text Mining and Social Network
Analysis:
• Text Mining
• Topic Modelling
• Sentiment Analysis
• Social Network Analysis
• Useful guide
Case Studies
24/04/2017 © The University of Sheffield
35
DHL #AfricaAsOne
• Wanted to increase awareness and target
influencers
• They were able to find over 65 thousand
influencers across 45 African Countries
• Secured a reach of 1,200,750,000, with an
advertising value equivalent of
£12,112,8671
24/04/2017 © The University of Sheffield
36
DHL #AfricaAsOne
• They used Audiense to create highly
customised groups of Twitter users
• “In-depth filtering and segmentation of
Twitter users by keywords in their bio,
combined with other variables1”
1 https://audiense.com/case-studies/dhl-africaasone/
24/04/2017 © The University of Sheffield
37
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
Further Case Studies
• List of case studies from Audiense
• List of case studies from Visibrain
24/04/2017 © The University of Sheffield
39
Summary
• We looked at social media analytics and
focused mostly on Twitter due to the open
nature of the platform it is a widely used
platform.
• The Importance of Social Media Analytics
for Businesses (video)
24/04/2017 © The University of Sheffield
40
Practical on Wednesday
24/04/2017 © The University of Sheffield
41
• TrendsMap
• Audiense
• NodeXL Graph Gallery

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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.
  • 3. 24/04/2017 © The University of Sheffield 3 https://wasimahmed.org/about/ http://blogs.lse.ac.uk/impactofsocialsciences/?s=wasim+ahmed Published a number of research papers, and blogged widely.
  • 4. 24/04/2017 © The University of Sheffield 4 • Delivered a number of talks related to my research such as to the government , media , and industry. • Upcoming talk to delegates at the European Centre for Nuclear Research at CERN in Geneva. June, 2017. • Co-running a Summer School in Šibenik, Croatia on social media analytics. June 2017. Recent and Upcoming talks
  • 5. Lecture Aims • Develop knowledge on the types of social media analytics that are possible. • Gain an overview of social media analytics tools. • Understand how social media analytics have been put to use by organisations. 24/04/2017 © The University of Sheffield 5
  • 6. 24/04/2017 © The University of Sheffield 6 • Twitter has over 313 million monthly active users1 – consumers can use this channel to express their views. • Businesses spend millions every year tailoring their brands and protecting them. • Brands can use Twitter to tap into and target consumers and may spend a lot of money in doing so. 1 https://about.twitter.com/company Twitter
  • 7. 24/04/2017 © The University of Sheffield 7 • Open API so anyone with an Internet connection can retrieve data • Open platform where anyone can follow anyone and can request to follow other users • A lot of meta-data fields available to developers to create analytics apps Why is Twitter so popular?
  • 8. 24/04/2017 © The University of Sheffield 8 United Airlines • Recent example of a PR disaster • To-date this has received n=170,346 retweets and n=151,014 likes
  • 9. 24/04/2017 © The University of Sheffield 9 Immediate Twitter Aftermath of United Airlines - NodeXL Network Graph
  • 10. 24/04/2017 © The University of Sheffield 10 American Airlines
  • 11. 24/04/2017 © The University of Sheffield 11 American Airlines (Heat Map using TrendsMap)
  • 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/
  • 16. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter networks
  • 17. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter networks
  • 18. 24/04/2017 © The University of Sheffield 18 How can you use this? • You can use social network analysis to identify influencers and people who are interested in a particular topic. • You can identify clusters of users interested in a particular topic and use automated methods to target them.
  • 20. 24/04/2017 © The University of Sheffield 20 In-built Twitter Analytics
  • 21. 24/04/2017 © The University of Sheffield 21 Software As a Service (SAS) • A number of online tools have emerged utilising Twitter data and generated good revenue by selling analytics as a service. • Services such as: • Brand and Media Monitoring • Consumer Engagement • Security
  • 22. 24/04/2017 © The University of Sheffield 22 Visibrain Audiense TrendsMap Echosec DiscoverText NodeXL
  • 23. 24/04/2017 © The University of Sheffield 23 IBM Watson Personality Insights https://www.ibm.com/watson/developercloud/pe rsonality-insights.html#how-it-works-block
  • 24. 24/04/2017 © The University of Sheffield 24 Twitter as a Media Monitoring Tool
  • 25. 24/04/2017 © The University of Sheffield 25 Monitoring Keywords for Brand Monitoring
  • 26. 24/04/2017 © The University of Sheffield 26 Monitoring Keywords for Brand Monitoring
  • 27. 24/04/2017 © The University of Sheffield 27 Social Media For Security – Monitoring Locations
  • 28. 24/04/2017 © The University of Sheffield 28 Social Media For Security – Monitoring Locations
  • 29. 24/04/2017 © The University of Sheffield 29 Social Media Analytics For Research (DiscoverText) Over 150 mentions of DiscoverText in academic research
  • 30. 24/04/2017 © The University of Sheffield 30 DiscoverText – Powerful Filtering
  • 31. 24/04/2017 © The University of Sheffield 31 Social Media Analytics for Consumer Engagement (Audiense)
  • 32. 24/04/2017 © The University of Sheffield 32 Social Media Analytics for Consumer Engagement (Audiense) • You can leverage the back end analytics provided by Twitter to build specific audiences. • You can use this information target users and monitor the performance of the message that you send.
  • 33. 24/04/2017 © The University of Sheffield 33 Filter and Target
  • 34. 24/04/2017 © The University of Sheffield 34 Using R for Text Mining and Social Network Analysis: • Text Mining • Topic Modelling • Sentiment Analysis • Social Network Analysis • Useful guide
  • 35. Case Studies 24/04/2017 © The University of Sheffield 35
  • 36. DHL #AfricaAsOne • Wanted to increase awareness and target influencers • They were able to find over 65 thousand influencers across 45 African Countries • Secured a reach of 1,200,750,000, with an advertising value equivalent of £12,112,8671 24/04/2017 © The University of Sheffield 36
  • 37. DHL #AfricaAsOne • They used Audiense to create highly customised groups of Twitter users • “In-depth filtering and segmentation of Twitter users by keywords in their bio, combined with other variables1” 1 https://audiense.com/case-studies/dhl-africaasone/ 24/04/2017 © The University of Sheffield 37
  • 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
  • 39. Further Case Studies • List of case studies from Audiense • List of case studies from Visibrain 24/04/2017 © The University of Sheffield 39
  • 40. Summary • We looked at social media analytics and focused mostly on Twitter due to the open nature of the platform it is a widely used platform. • The Importance of Social Media Analytics for Businesses (video) 24/04/2017 © The University of Sheffield 40
  • 41. Practical on Wednesday 24/04/2017 © The University of Sheffield 41 • TrendsMap • Audiense • NodeXL Graph Gallery