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Social Media: A Practical Approach

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Researching Social Media: A Practical Overview. Delivered at a workshop on Social Media theory and practice at the University of Sheffield. Tools that were outlined were Mozdeh, TAGS, COSMOS, Chorus as well as DiscoverText and NodeXL Pro.

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Social Media: A Practical Approach

  1. 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. 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.
  3. 3. 30/05/2017 © 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. 4. 30/05/2017 © The University of Sheffield 4 • 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. 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. 30/05/2017 © The University of Sheffield 5
  6. 6. Social Media Platforms 30/05/2017 © The University of Sheffield 6 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Number of Million Monthly Active Users
  7. 7. 30/05/2017 © 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. 8. Social Media Platforms 30/05/2017 © The University of Sheffield 8 • 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. 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. 30/05/2017 © The University of Sheffield
  10. 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. 30/05/2017 © The University of Sheffield
  11. 11. Types of Analysis • Content Analysis • Thematic Analysis • Network Analysis • Machine Learning • Sentiment Analysis 30/05/2017 © The University of Sheffield 11
  12. 12. 30/05/2017 © The University of Sheffield Tools Covered in this Presentation • DiscoverText • NodeXL • Chorus • Mozdeh • TAGS • COSMOS
  13. 13. DiscoverText 30/05/2017 © The University of Sheffield 13 • 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. 14. DiscoverText used in… • Consumer industries • Education • Human Resources • Legal • Medical & Pharma • Government • Military 30/05/2017 © The University of Sheffield 14
  15. 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 30/05/2017 © The University of Sheffield 15
  16. 16. Fiver Pillars of Text Analytics • Search • Filtering • De-duplication and Clustering • Human Coding • Machine-Learning 30/05/2017 © The University of Sheffield 16
  17. 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. 30/05/2017 © The University of Sheffield 17
  18. 18. Filtering Data 30/05/2017 © The University of Sheffield 18
  19. 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 30/05/2017 © The University of Sheffield 19
  20. 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 30/05/2017 © The University of Sheffield 20
  21. 21. 30/05/2017 © The University of Sheffield 21 Import Twitter data
  22. 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 30/05/2017 © The University of Sheffield 22
  23. 23. Importance of Generating Clusters 30/05/2017 © The University of Sheffield 23
  24. 24. #WorldMentalHealthDay
  25. 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
  26. 26. [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
  27. 27. [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
  28. 28. 30/05/2017 © The University of Sheffield 28 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.
  29. 29. 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/
  30. 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/
  31. 31. 30/05/2017 © The University of Sheffield 31 Theresa May (29th May)
  32. 32. 30/05/2017 © The University of Sheffield 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.
  33. 33. 30/05/2017 © The University of Sheffield Chorus • This is the layout of Chorus Tweet Catcher
  34. 34. Chorus • This is the layout of Chorus Tweet Vis 30/05/2017 © The University of Sheffield
  35. 35. Chorus Tutorials • Chorus manual here • Great video overview of Chorus here 30/05/2017 © The University of Sheffield
  36. 36. 30/05/2017 © The University of Sheffield 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. 37. Mozdeh 30/05/2017 © The University of Sheffield • An example time series graph of 5,055,299 tweets related to norovirus
  38. 38. 30/05/2017 © The University of Sheffield 38 Time Series Graphs
  39. 39. Mozdeh Tutorials • Great user guide here • Great theoretical overview here 30/05/2017 © The University of Sheffield
  40. 40. 30/05/2017 © The University of Sheffield 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/
  41. 41. 30/05/2017 © The University of Sheffield 41
  42. 42. 30/05/2017 © The University of Sheffield TAGS – Twitter Archiving Google Sheet
  43. 43. 30/05/2017 © The University of Sheffield 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
  44. 44. 30/05/2017 © The University of Sheffield COSMOS Project • Some of the features include generating: • Word Clouds • Frequency Charts • Network Graphs • Geographical Maps of Tweets
  45. 45. 30/05/2017 © The University of Sheffield COSMOS Project Layout
  46. 46. 30/05/2017 © The University of Sheffield COSMOS Tutorials • Great video tutorial(s) here
  47. 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 30/05/2017 © The University of Sheffield 47
  48. 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 30/05/2017 © The University of Sheffield 48
  49. 49. 30/05/2017 © The University of Sheffield 49 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
  50. 50. 30/05/2017 © The University of Sheffield 50 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. 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 30/05/2017 © The University of Sheffield 51
  52. 52. Questions? • Tweet me! @was3210 • Questions related to the tools? • TAGS = @mhawksey • NodeXL = @marc_smith • COSMOS = @pbFeed • Mozdeh = @mikethelwall • DiscoverText = @StuartWShulman 30/05/2017 © The University of Sheffield
  53. 53. To Discover And Understand.

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