1. Communicating Science
Through Social Media:
Tools and Techniques
Wasim Ahmed (BA, MSc)
Twitter (was3210)
Email: wahmed1@Sheffield.ac.uk
Saturday 13th of May 2017

2. About me
• Third Year PhD student in the Health Informatics Research
Group, Information School, University of Sheffield. (Faculty
Scholarship).
• Worked as a Teaching Assistant and Research Associate
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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• Delivered a number of talks related to my research such
as to the government , media , and industry.
• Featured in White Rose DTC Researchers in the News
(2014/2015/2016)
• Upcoming talk to delegates at the European Centre for
Nuclear Research at CERN in Geneva. June, 2017.
Recent and Upcoming talks

5. Aims
• Provide an overview of how I utilised social
media and blogging to reach new audiences.
• Gain new insight on strategies you can adopt in
order to potentially attract new readership
• Develop knowledge on the tools to support
dissemination as well as social media research
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6. Initial thoughts on Twitter
• Was sceptical of social media and Twitter –
wasn’t aware of the potential
• Fear of the unknown and having a public facing
account
• Joined in 2014 when I started my PhD and
followed the #PhDChat and #NSMNSS hashtags
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7. Starting out
• Started off tweeting news articles and papers
related to social media
• Would use Twitter to tweet during conferences
and network (I know you from Twitter…)
• Found it useful for asking questions about
research e.g., asking if people know about
papers related to a specific topic
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8. Account Would Grow Overtime
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Twitter Analytics

10. Why I Started to Blog
• Found that it can take a long time to publish
journal and/or conference papers
• Started a research blog which facilities instant
publishing
• Also acted as a way of linking publications and
workshop slides in one place
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11. Why I started to blog
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12. Linked the blog to my Twitter
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• If you like and/or retweet
another user they may
click on your account to
see who you are. This may
convert to blog readers
which may also convert to
people who click on your
academic work

13. Twitter Visitors Convert to Blog
Readers
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14. Twitter Visitors Convert to Blog
Readers
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Retweet from journalist

15. Twitter and Blog Visitors Convert to
Research Gate Readers
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Academic work
Twitter
Blogging

16. Examples
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17. Result
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18. Other Social Media Platforms
• Cross post to LinkedIn
• Cross post to departmental blog
• Occasionally cross post to Facebook
• Not found a use for Instagram (yet)
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19. Tips for starting out on Twitter
• Start a Twitter account and Tweet
interesting content
• Find other researchers in your discipline
and/or the community hashtag e.g.,
#NSMNSS
• Start a hashtag and/or a community if one
does not exist for your discipline
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20. Avoid Sending Tweets Like This
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21. Do send tweets like this
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22. • Guest blog (LSE Impact) and/or create
your own blog (WordPress/ Medium)
• Make sure to adjust your writing to a non-
academic audience
• Connect your blog to your Twitter and
make sure to forward to people who may
be interested in reading it
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Tips for blogging

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“The LSE Impact Blog is a hub for researchers,
administrative staff, librarians, students, think
tanks, government, and anyone else
interested in maximising the impact of
academic work in the social sciences and other
disciplines. We hope to encourage debate, share
best practice and keep the impact community up
to date with news, events and the latest
research.”

24. Guest Blogging for the London School of
Economics and Political Sciences Impact
Blog
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• My most popular post has over 20 thousand
page views and cited in journals, conferences,
and a PhD thesis
• Led to event invitations, and increased visibility
of my academic work as blog has a strong
readership
• Very friendly team and editors who are
extremely supportive

25. Tools to Facilitate
Communication
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Social Media Analytics for Increasing Engagement (Audiense)

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Social Media Analytics for Consumer Engagement
(Audiense)
• You can leverage the metadata provided by
Twitter to build specific audiences.
• You can use this information target users and
monitor the performance of the message that
you send.

28. DHL #AfricaAsOne
• Wanted to increase awareness and target
influencers – used Audiense
• They were able to find over 65 thousand
influencers across 45 Countries
• Secured a reach of 1,200,750,000, with an
advertising value equivalent of
£12,112,8671
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Filter and Target

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Audiense Results

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Social Elephants

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Social Elephants

33. Social Elephants - Examine Reach
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34. Trial for Social Elephants and
Audiense
• Social Elephants 2 Month Trial use code:
SRUK and email
support@socialelephants.com with code in
subject.
• Audiense Community Manager 14 days
https://buy.audiense.com/trial/new
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• Twitter has over 313 million monthly active users1 –
consumers 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

36. 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

37. DiscoverText used in…
• Consumer industries
• Education
• Human Resources
• Legal
• Medical & Pharma
• Government
• Military
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38. 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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39. Fiver Pillars of Text Analytics
• Search
• Filtering
• De-duplication and Clustering
• Human Coding
• Machine-Learning
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40. 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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41. Finding influencers
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42. 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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43. 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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44. 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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Import Twitter data

46. 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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47. Importance of generating clusters
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48. DiscoverText
• Request a 3 day trial
• http://discovertext.com/start-a-free-trial/
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49. Conclusion
• Provided an overview of how I utilised social
media and blogging to reach new audiences.
• Provided a list of strategies you can adopt in
order to potentially attract new readership
• Outlined tools to support scientific dissemination
as well as social media research
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50. Questions?
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51. To
Discover
And
Understand.