More Related Content Similar to Social Media: A Practical Approach (20) Social Media: A Practical Approach 1. Social Media: A
Practical Approach
Wasim Ahmed (BA, MSc)
@was3210
wahmed1@sheffield.ac.uk
Tuesday 30th of May 2017
Researching Social Media: A Theoretical and Practical
Overview - University of Sheffield
2. About me
• Third Year PhD student in the Health Informatics Research
Group, Information School, University of Sheffield. (Faculty
Scholarship).
• Worked on a number of projects teaching and researching
social media.
• Run an analytics blog with readership in over 136 countries.
Read across media, government, and academia.
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https://wasimahmed.org/about/
http://blogs.lse.ac.uk/impactofsocialsciences/?s=wasim+ahmed
Published a number of
research papers, and
blogged widely.
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• Twitter has over 313 million monthly active users1 –
citizens can use this channel to express their views.
• Research on Twitter has the potential to cut across many
disciplines.
• Questions arise over how to obtain and analyse social
media data.
1 https://about.twitter.com/company
Twitter for Academic Research
5. Twitter as a Consumer Panel
• 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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• 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. Social Media Platforms
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• Facebook (1.871 billion monthly active users)
• YouTube (1 billion monthly active users)
• Instagram (600 million monthly active users)
• Twitter (317 million monthly active users)
• Pinterest (150 million monthly active users)
9. Twitter API
• 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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10. 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.
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11. Types of Analysis
• Content Analysis
• Thematic Analysis
• Network Analysis
• Machine Learning
• Sentiment Analysis
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Tools Covered in this Presentation
• DiscoverText
• NodeXL
• Chorus
• Mozdeh
• TAGS
• COSMOS
13. DiscoverText
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• This presentation will focus on the potential of
DiscoverText for analysing Twitter data for academic
research.
• However, there are many more potential uses of
DiscoverText
14. DiscoverText used in…
• Consumer industries
• Education
• Human Resources
• Legal
• Medical & Pharma
• Government
• Military
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15. 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
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16. Fiver Pillars of Text Analytics
• Search
• Filtering
• De-duplication and Clustering
• Human Coding
• Machine-Learning
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17. For a topic overview you could
• Retrieve Twitter data on a topic of interest
search and filter out non-relevant data.
• Generate duplicates and near-duplicate
clusters.
• This would allow you to more easily code
the data.
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19. DiscoverText has Active Learning
• You can 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
• So humans and machines work together
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20. An example: Manchester Derby
• During a football game users were tweeting
about a buzzing sound, and some were not
happy with Sky’s camera angles
• You could use DiscoverText to filter the
data
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22. Applying Text Analytics
• Search for ‘buzzing’, ‘noise’, and ‘camera’
• Find positive instances (‘what’s that buzzing
noise from Sky?) and also negative e.g., people
‘buzzing’ from the game, or which team is
making the most ‘noise’
• Generating clusters and coding the data
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25. • 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
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How Can You Use This?
• You can use social network analysis to
identify influencers and people who are
interested in a particular topic and you can
examine the content they share.
• You can identify clusters of users interested in
a particular topic and use automated methods
to target them.
30. 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/
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Chorus Analytics Tweetcatcher
Desktop Edition
• Chorus-TCD is a product of Brunel University
which allows you to retrieve and analyse data.
• Uses Twitter’s Search API.
• Great video introduction here.
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Chorus
• This is the layout of Chorus Tweet Catcher
34. Chorus
• This is the layout of Chorus Tweet Vis
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Mozdeh
• Mozdeh is a product of the ‘Statistical
Cybermetrics Research Group’ at the University
of Wolverhampton.
• Mozdeh is a Windows desktop program that can
gather tweets by automatically searching for
keywords associated with a topic.
37. Mozdeh
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• An example time series graph of 5,055,299
tweets related to norovirus
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TAGS – Twitter Archiving
Google Sheets
• TAGS is a free Google Sheet template which
lets you setup and run automated collection
of search results from Twitter.
• Set up TAGS here https://tags.hawksey.info/get-
tags/
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TAGS – Twitter Archiving
Google Sheet
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COSMOS Project
• The Collaborative Online Social Media
Observatory (COSMOS): Social Media and Data
Mining is an ESRC project a part of the strategic
Big Data investment.
• The COSMOS Project (Burnap et al, 2014) uses
the Streaming API
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COSMOS Project
• Some of the features include generating:
• Word Clouds
• Frequency Charts
• Network Graphs
• Geographical Maps of Tweets
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COSMOS Tutorials
• Great video tutorial(s) here
47. NVivo
• You can import social media data captured
elsewhere into NVivo
• Or you can use Ncapture within NVivo to
pull in data
• Useful for content analysis and thematic
analysis
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48. Summary
• This presentation has provided an
overview of some free and paid tools that
can be used to capture and analyse
Twitter data
• Different tools allow you to perform
different types of analysis
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Prices
• Mozdeh, TAGS, COSMOS, and Chorus
are FREE
• DiscoverText (Professional) $49 a month
for academics and $24 a month for
students
• NodeXL Pro $199 a year for academics
and $29 a year for students
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Summer School
• 3-day intensive Summer School on social
media analytics taking place in Sibenik,
Croatia .June 28th to June 30th 2017
• More information here:
https://event.gg/5776/
51. iConference 2018 in Sheffield
• The theme of iConference 2018, Transforming
Digital Worlds, will be the importance of the
information field in transforming the increasingly
data-driven world.
• Run by a consortium of Information Schools
dedicated to advancing the information field
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52. Questions?
• Tweet me! @was3210
• Questions related to the tools?
• TAGS = @mhawksey
• NodeXL = @marc_smith
• COSMOS = @pbFeed
• Mozdeh = @mikethelwall
• DiscoverText = @StuartWShulman
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