The talk provides an overview of a number of emerging social listening and digital engagement tools such as Visibrain, Audiense, Echosec, Social Elephants, NodeXL, and DiscoverText among others. It provides an overview of a number of tools that are freely available to academic researchers such as Mozdeh, Chorus, TAGS, COSMOS, and Netlytic among others. The talk highlights a number of different research methods that have been utilised by academic researchers, such as machine learning, sentiment analysis, network analysis, and content and thematic analysis which can be utilised to be applied to the domains of commercial data analytics as well as academic research. The talk also touches on the diverse potentials of social data for organisations from forecasting, detecting crisis events, and as an early warning system for organisational threats.

1. Gaining Powerful
Insights into Social
Media Listening
Wasim Ahmed (BA, MSc)
PhD Supervisors: Professor Peter Bath,
Dr Laura Sbaffi, and Dr Gianluca Demartini
Boston University, College of Communication
October 20th 2017
@was3210
wahmed1@sheffield.ac.uk

2. About Me
• ThirdYear PhD student (Faculty
Scholarship) in the Health
Informatics Research Group,
Information School, University of
Sheffield (UK).
• Worked on a number of exciting
projects teaching and researching
social media with organisations such
as Manchester United.
• Run an analytics blog with
readership in over 136 countries.
Read across media, government,
and academia.

3. Emergence of Social Media
• Information and
communication technology
has transformed significantly
due to the emergence of
social media.
• The speed of the
transformation has occurred
rapidly and due to the advent
of mobile devices this meant
people could share from
anywhere, at any time.
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5.052 billion unique mobile users

5. Global Digital Snapshot
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7.524 billion total estimated global population
3.819 billion total estimated Internet users
3.028 billion total social media users

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Time Spent on Mobile & Social
Media is Increasing

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• The average mobile phone users touches
their phone 2,617 times a day.
• The time spent on social media has been
increasing overtime with an average of 2
hours a day.
Time Spent on Mobile & Social
Media

9. • Interviews and surveys may take long time to
devise and implement.
• Now social media data provide unparalleled
insight for brands.
• Vast amounts of data is generated and this
presentation outlines methods and tools of
analysing this data.
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Marketing Potential

10. Data Generated by Twitter
• According to one statistic there are on average 6
thousand tweets a second!
• So around 350,000 tweets are sent every
minute.
• Which makes it around 500 million tweets per
day.
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Social Media for
Marketing

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Social Media in Academia

13. Uses of Social Media in Academia
• Teaching
• Scholarly Communication
• Health Research
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14. Uses of Social Media in Academia
• Marketing
• Library Use
• Study of Politics and Political
Uprisings
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15. Methods to Analyse Social Media Data
• Content Analysis
• Thematic Analysis
• Network Analysis
• Machine Learning
• Sentiment Analysis
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16. Content Analysis
• Can be used for systematically labelling text, audio, and/or visual
communication, and provides a numerical output.
• It has been used to understand narratives in newspapers,
magazines, television, videos, and also the Internet.
• In the context of social media it can be used to systematically label
social media posts such as tweets, and it is a popular method.
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17. Thematic Analysis
• Thematic analysis involves a rigorous process in order to locate
patterns within data through data familiarisation, coding, and
developing an revising themes.
• Similar to content analysis, but involves labelling all data, for
example, all tweets in a dataset.
• Not as popular as content analysis for social media research,
however, it has the potential to provide greater depth and uncover
more themes.
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18. Social Network Analysis
• Social network analysis can be used to measure and map the
relationships between individuals, organisations, Web Pages, and
information and/or knowledge entities.
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Degree Centrality Betweenness Centrality
See Richard Ingram’s blog post visualising Data: Seeing is Believing
http://www.richardingram.co.uk/2012/12/visualising-data-seeing-is-believing

19. Machine Learning
• A kind of artificial intelligence which allows computers to
learn without being programmed.
• Involves humans labelling a subset of data and allows
the computer to learn and code the remainder of the
data.
• Useful and used widely in research because of the
volume of data generated from social media.
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20. Sentiment Analysis
• Sentiment Analysis can be used to find out whether a
piece of text is either positive, negative, and/or neutral.
• It is possible to use existing word lists with positive and
negative words and/or build custom lists based on
specific datasets.
• Useful to gauge how people feel about a specific topic
and/or event in real-time.
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Software As a Service (SAS)
• A number of online platforms have
emerged utilising social data by selling
analytics as a service such as:
• Brand and Media Monitoring
• Consumer Engagement
• Security

22. No. of Million Active Social Media Users
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0
200
400
600
800
1000
1200
1400
1600
1800
2000
Number of Million Monthly Active Users

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• 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?

25. Twitter Application Programming
Interface (API)
• APIs allow developers to interact with a particular
technology.
• Twitter’s Search API (free)– is a sample of tweets so
some tweets and users may be missing from results.
This is free, but limited to 7 days back in time.
• Firehose API (paid) – in theory, 100% of Twitter data.
This can be costly.
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26. How Do You Retrieve Data?
• Use a keyword e.g., Ebola.
• Use a hashtag e.g., #EbolaOutbreak.
• Use a Twitter handle e.g., @was3210.
• Combine search queries using AND or OR
operators.
• There are other operators.
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Applications & Tools Covered
• DiscoverText
• Audiense
• Visibrain
• Echosec
• Social Elephants
• NodeXL
• Chorus
• Mozdeh
• TAGS
• COSMOS
• Netlytic

28. DiscoverText
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• This presentation looks at the potential of DiscoverText
for analysing social media data.
• However, there are many more potential uses of
DiscoverText.

29. Uses of DiscoverText
• Consumer industries
• Education
• Human Resources
• Legal
• Medical & Pharma
• Government
• Military
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30. DiscoverText as Data Science
• DiscoverText has a number of very powerful text
mining, human coding, and machine learning
features.
• Access to the free Twitter Search API data.
• Access to premium Gnip PowerTrack 2.0
Twitter data (ability to filter the full Twitter
firehose).
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31. Fiver Pillars of Text Analytics
• Search
• Filtering
• De-duplication and Clustering
• Human Coding
• Machine-Learning
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32. Filtering Data
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33. Generating Clusters
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34. DiscoverText has Active Learning
• Manually code a sub-set of data in
DiscoverText then allow a machine to
code the next iteration.
• You can check for quality (adjust coding
parameters) and run the cycle again.
• Humans and machines work together.
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Retrieve and/or Import data from a number of platforms

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Exciting Development
• DiscoverText is beta testing the ability to
export to NodeXL.
• The new functionality that creates
GraphML files based on Twitter data.

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• Network Overview, Discovery, and Exploration
for Excel (NodeXL) is a graph visualization tool.
• Allows the extraction of data from a number of
popular social media platforms including
Twitter, YouTube, and Facebook.
• Instagram capabilities in beta.
NodeXL

38. #WorldMentalHealthDay

39. [Divided]
Polarized Crowds
[Unified]
Tight Crowd
[Fragmented]
Brand Clusters
[Clustered]
Community Clusters
[In-Hub & Spoke]
Broadcast Network
[Out-Hub & Spoke]
Support Network
Six kinds of Twitter networks

40. [Divided]
Polarized Crowds
[Unified]
Tight Crowd
[Fragmented]
Brand Clusters
[Clustered]
Community Clusters
[In-Hub & Spoke]
Broadcast Network
[Out-Hub & Spoke]
Support Network
Six kinds of Twitter networks

41. • 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 conversing about.
Further Interesting Insights Produced by
NodeXL

42. NodeXL Graph Gallery
http://nodexlgraphgallery.org

43. Visibrain
• Visibrain is a media monitoring tool which
has access to the Twitter Firehose.
• Ability to set up report delivery, and alerts
when there are a burst of posts around a
topic.
• For example, an alert if there is a flurry of
tweets around ‘hacking’ and a bank.
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Twitter as a Media Monitoring Tool

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Monitoring Keywords

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Reason for the Peak

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Crisis Alerts using
Visibrain

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Social Media Analytics for Consumer
Engagement

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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.

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IBM Watson Personality Insights
• Potential to extract personality characteristics
based the written text of a user (1200 words is
recommended)
• Uses personality traits and tailors individuals
to other individuals, opportunities, products
and/or customized messages.

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Filter and Target in
Audience
There are a number of
methods of filtering and
searching all Twitter users

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Social Media For Security – Mon
Locations

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Social Media For Security – Monitoring
Locations

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Social Elephants

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Social Elephants

58. Examine Reach
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Chorus Analytics Tweetcatcher
Desktop Edition (TCD)
• Chorus-TCD is a product of Brunel University
which allows you to retrieve and analyse Twitter
data.
• Chorus uses Twitter’s Search API.
• It is available to use for free for non commercial
use.

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Chorus Layout
Keywords to
retrieve data are
entered here

61. Chorus Tweet Vis Layout
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Mozdeh
• Mozdeh is a product of the ‘Statistical Cybermetrics
Research Group’ at the University of Wolverhampton led
by Prof. Mike Thelwall.
• A Windows desktop program that can gather tweets by
searching for keywords associated with a topic.
• Create time series graphs, network graphs, and perform
sentiment analysis.

63. Mozdeh Layout
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• An example time series graph of 5,055,299
tweets related to norovirus
Enter keywords to
search
Search Feature
Results

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Time Series Graphs in Mozdeh

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Twitter Archiving Google Sheets
(TAGS)
• TAGS is a free Google Sheet template.
• Allows you to setup and run automated
collection of search results from Twitter.
• Created and maintained by Martin Hawksey.

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66Twitter Archiving Google
Sheets (TAGS)

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Twitter Archiving Google
Sheet (TAGS)

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The Collaborative Online Social
Media Observatory (COSMOS)
• Features include: generating word clouds, frequency
charts, network graphs, and plotting tweets to locations.
• The application allows the ability to switch between the
Search API and the Streaming API.

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COSMOS Project User
Interface

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Netlytic Features
• Retrieve data from: social media sites (Twitter,
Facebook, YouTube, Instagram, RSS Feed &
text/csv file).
• Perform analysis such as: text Analysis, network
analysis, and create reports which map
geotagged posts.

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Social Network Analysis

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Netlytic Reports

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Over 3 Billion Images Shared On Social Media
Every Day

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Webometric Analyst
• There are tools that have emerged which can be
utilised for analysing images on social media.
• Webometric Analyst can be used to
download images from Twitter or Tumblr.
• It will also create lists of the most frequently
downloaded identical images.

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Webometric Analyst

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Netra Systems

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Netra Systems

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Access to Tools For Delegates
• If delegates are interested in any of the tools
outlined in this presentation there are trials that
the tools offer.
• Happy to make an introduction and/or answer
any questions on the functionality of the tools
during the workshop.

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There are many other tools
• Crimson Hexagon
• Brandwatch
• Social Bakers
• Simply Measured
• Talk Walker
• Sprout Social

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Other Trends
• Virtual Reality (Facebook’s Oculus
technology)
• Online Dating
• Social Shopping (buy buttons)

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Other Trends
• Mobile Wallets
• Live Streaming
• Predictive Analytics

83. Ethical Approval in Academia
• Require researchers to obtain ethics approval
before collecting data. May make it difficult to
study emerging events.
• Might not be possible to gain consent from all
users in a dataset.
• Researchers may need to alter posts when
reporting results to prevent people being
identified. This may change the meaning of the
post.
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Social Media Research in
Academic Context
• Care placed on protecting participants
from harm. Overtime, potentially most UK
universities will require researchers to
obtain ethics approval.
• When data is analysed things may emerge
from the data that may draw attention to
groups, individuals, and trends.

85. Industry Research
• May be focused on speed and responding
rapidly to events in the first instance.
• May not be as concerned with privacy issues
i.e., user-handles may be disclosed.
• User may be targeted for commercial gain, for
example, users who tweet they are feeling down
may be offered a product to help them feel
better.
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Conclusion
• Social Media platforms have emerged
as important communication devices
• Existing Social Media tools can be
utilised to extract insight from these
platforms
• Social media platforms have emerged as
important communication devices in the 21st
century.
• They generate vast amounts of data that can be
analysed for academic and commercial insight.
• This talk has highlighted some of the tools and
techniques used to analyse this data.
Conclusion

87. Questions?

88. References
• Data from Slide 8 from https://blog.dscout.com/mobile-touches
• Images on slide 20 from Richard Ingram’s blog post visualising Data:
Seeing is Believing http://www.richardingram.co.uk/2012/12/visualising-
data-seeing-is-believing/
• Data from Slide 6 and 7 is from
https://www.slideshare.net/wearesocialsg?utm_campaign=profiletracking&ut
m_medium=sssite&utm_source=ssslideview
• Data from slide 24 is from http://www.internetlivestats.com/twitter-statistics/
• Copyright Free Images from https://www.pexels.com/
• Background on research methods from: Bryman, A. (2008). Social
Research Methods. Social Research, 3. doi: 10.4135/9781849209939.
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