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Practical Tools Social Media For Consumer Insight (Guest Lecture)

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A guest lecture to students on a module e-business and e-commerce at the Information School, University of Sheffield. We specifically looked at the potential DiscoverText for providing insight into Twitter data. However, there are many potential uses of DiscoverText.

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Practical Tools Social Media For Consumer Insight (Guest Lecture)

  1. 1. Social Media Practical Wasim Ahmed (@was3210) Email: wahmed1@Sheffield.ac.uk Guest Lecture for INF6003 E-Business and E-Commerce Monday 27th April 2017
  2. 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. 3. 27/04/2017 © 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. 4. 27/04/2017 © 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. 5. Acknowledgements • Dr Stuart Shulman, Founder and CEO, Texifter, LLC • There are a number of excellent Text Analytics presentations on Stu’s SlideShare account • Also this great online workshop 28/04/2017 © The University of Sheffield 5
  6. 6. 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. 27/04/2017 © The University of Sheffield 6
  7. 7. 27/04/2017 © The University of Sheffield 7 • 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
  8. 8. 27/04/2017 © The University of Sheffield 8 • 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?
  9. 9. 27/04/2017 © The University of Sheffield 9 United Airlines • Recent example of a PR disaster • To-date this has received n=170,346 retweets and n=151,014 likes
  10. 10. 27/04/2017 © The University of Sheffield 10 American Airlines
  11. 11. 27/04/2017 © The University of Sheffield 11 In-built Twitter Analytics
  12. 12. 27/04/2017 © The University of Sheffield 12 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
  13. 13. 27/04/2017 © The University of Sheffield 13 Visibrain Audiense Echosec DiscoverText NodeXL Echosec
  14. 14. DiscoverText 27/04/2017 © The University of Sheffield 14 • This lecture will focus on the potential of DiscoverText for analysing Twitter data for commercial use. • However, there are many more potential uses of DiscoverText
  15. 15. DiscoverText used in… • Consumer industries • Education • Human Resources • Legal • Medical & Pharma • Government • Military 28/04/2017 © The University of Sheffield 15
  16. 16. 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 28/04/2017 © The University of Sheffield 16
  17. 17. Fiver Pillars of Text Analytics • Search • Filtering • De-duplication and Clustering • Human Coding • Machine-Learning 28/04/2017 © The University of Sheffield 17
  18. 18. How can this be used? • In the aftermath of United Airlines and American Airlines incidents there were millions upon millions of tweets • So we need smarter ways to tackle the data and to extract the intelligence that we need. Text Analytics can help us with this. 28/04/2017 © The University of Sheffield 18
  19. 19. How can this be used? • The airline might want to know the overall discussion taking place i.e., a topic overview. • They might want to know where people were tweeting from as well as whether they are influential. 28/04/2017 © The University of Sheffield 19
  20. 20. For a topic overview you could • Retrieve Twitter data on the day of the crisis, search and filter out non-relevant data. • Generate duplicates and near-duplicate clusters. • This would allow you to more easily code the data. 28/04/2017 © The University of Sheffield 20
  21. 21. Finding influencers 28/04/2017 © The University of Sheffield 21
  22. 22. 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 27/04/2017 © The University of Sheffield 22
  23. 23. 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 28/04/2017 © The University of Sheffield 23
  24. 24. An example: Manchester Derby • During yesterday’s Manchester Derby users were tweeting about a buzzing sound, and some were not happy with Sky’s camera angles • Vanity analytics such as heat-maps, tweet volumes only tell you so much. Text-analytics can provide a deeper understanding with a scientific basis. 28/04/2017 © The University of Sheffield 24
  25. 25. 27/04/2017 © The University of Sheffield 25 Import Twitter data
  26. 26. Applying Text Analytics • Search for ‘buzzing’, ‘noise’, and ‘camera’ • Find positive instances and also negative e.g., people ‘buzzing’ from the game, or which team is making the most ‘noise’ • Generating clusters and coding the data 28/04/2017 © The University of Sheffield 26
  27. 27. Importance of generating clusters 27/04/2017 © The University of Sheffield 27
  28. 28. 28/04/2017 © The University of Sheffield 28 Let ‘s watch this video summary of the platform here
  29. 29. 27/04/2017 © The University of Sheffield 29 Twitter as a Media Monitoring Tool
  30. 30. 27/04/2017 © The University of Sheffield 30 Monitoring Keywords for Brand Monitoring
  31. 31. 27/04/2017 © The University of Sheffield 31 Monitoring Keywords for Brand Monitoring
  32. 32. 27/04/2017 © The University of Sheffield 32 Social Media For Security – Monitoring Locations
  33. 33. 27/04/2017 © The University of Sheffield 33 Social Media For Security – Monitoring Locations
  34. 34. 27/04/2017 © The University of Sheffield 34 Social Media Analytics for Consumer Engagement (Audiense)
  35. 35. 27/04/2017 © The University of Sheffield 35 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.
  36. 36. 27/04/2017 © The University of Sheffield 36 IBM Watson Personality Insights https://www.ibm.com/watson/developercloud/p ersonality-insights.html#how-it-works-block
  37. 37. 27/04/2017 © The University of Sheffield 37 Filter and Target
  38. 38. 27/04/2017 © The University of Sheffield 38 Using R for Text Mining and Social Network Analysis: • Text Mining • Topic Modelling • Sentiment Analysis • Social Network Analysis • Useful guide
  39. 39. Case Studies 27/04/2017 © The University of Sheffield 39
  40. 40. 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 27/04/2017 © The University of Sheffield 40
  41. 41. 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/ 27/04/2017 © The University of Sheffield 41
  42. 42. 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. 42 https://audiense-blog.s3.amazonaws.com/case- studies/World%20Economic%20Forum%20- %20Audiense%20Case%20Study%202016.pdf
  43. 43. Further Case Studies • Potential use cases from DiscoverText • List of case studies from Audiense • List of case studies from Visibrain 27/04/2017 © The University of Sheffield 43
  44. 44. 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) 27/04/2017 © The University of Sheffield 44
  45. 45. Practical Class (To be re-arranged due to scheduling conflict) 27/04/2017 © The University of Sheffield 45 • DiscoverText • NodeXL • Audiense • Trends Map • Follow the Hashtag
  46. 46. Practical Class (To be re-arranged due to scheduling conflict) 28/04/2017 © The University of Sheffield 46 • DiscoverText • NodeXL • Audiense • Trends Map • Follow the Hashtag
  47. 47. Links • Access DiscoverText here • Access NodeXL gallery here • Access TrendsMap here • Access Echosec free here 28/04/2017 © The University of Sheffield 47

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