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Keynote Talk - Gaining Powerful Insights into Social Media Listening

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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.

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Keynote Talk - Gaining Powerful Insights into Social Media Listening

  1. 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. 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. 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. 26/10/2017 © The University of Sheffield 3
  4. 4. 26/10/2017 © The University of Sheffield 4 5.052 billion unique mobile users
  5. 5. Global Digital Snapshot 26/10/2017 © The University of Sheffield 5 7.524 billion total estimated global population 3.819 billion total estimated Internet users 3.028 billion total social media users
  6. 6. 26/10/2017 © The University of Sheffield 6 Time Spent on Mobile & Social Media is Increasing
  7. 7. 26/10/2017 © The University of Sheffield 7
  8. 8. 26/10/2017 © The University of Sheffield 8 • 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. 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. 26/10/2017 © The University of Sheffield 9 Marketing Potential
  10. 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. 26/10/2017 © The University of Sheffield 10
  11. 11. 26/10/2017 © The University of Sheffield 11 Social Media for Marketing
  12. 12. 26/10/2017 © The University of Sheffield 12 Social Media in Academia
  13. 13. Uses of Social Media in Academia • Teaching • Scholarly Communication • Health Research 26/10/2017 © The University of Sheffield 13
  14. 14. Uses of Social Media in Academia • Marketing • Library Use • Study of Politics and Political Uprisings 26/10/2017 © The University of Sheffield 14
  15. 15. Methods to Analyse Social Media Data • Content Analysis • Thematic Analysis • Network Analysis • Machine Learning • Sentiment Analysis 26/10/2017 © The University of Sheffield 15
  16. 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. 26/10/2017 © The University of Sheffield 16
  17. 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. 26/10/2017 © The University of Sheffield 17
  18. 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. 26/10/2017 © The University of Sheffield 18 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. 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. 26/10/2017 © The University of Sheffield 19
  20. 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. 26/10/2017 © The University of Sheffield 20
  21. 21. 26/10/2017 © The University of Sheffield 21 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. 22. No. of Million Active Social Media Users 26/10/2017© The University of Sheffield 22 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Number of Million Monthly Active Users
  23. 23. 26/10/2017 © The University of Sheffield 23
  24. 24. 26/10/2017 © The University of Sheffield 24 • 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. 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. 26/10/2017 © The University of Sheffield
  26. 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. 26/10/2017 © The University of Sheffield
  27. 27. 26/10/2017 © The University of Sheffield Applications & Tools Covered • DiscoverText • Audiense • Visibrain • Echosec • Social Elephants • NodeXL • Chorus • Mozdeh • TAGS • COSMOS • Netlytic
  28. 28. DiscoverText 26/10/2017 © The University of Sheffield 28 • This presentation looks at the potential of DiscoverText for analysing social media data. • However, there are many more potential uses of DiscoverText.
  29. 29. Uses of DiscoverText • Consumer industries • Education • Human Resources • Legal • Medical & Pharma • Government • Military 26/10/2017 © The University of Sheffield 29
  30. 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). 26/10/2017 © The University of Sheffield 30
  31. 31. Fiver Pillars of Text Analytics • Search • Filtering • De-duplication and Clustering • Human Coding • Machine-Learning 26/10/2017 © The University of Sheffield 31
  32. 32. Filtering Data 26/10/2017 © The University of Sheffield 32
  33. 33. Generating Clusters 26/10/2017 © The University of Sheffield 33
  34. 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. 26/10/2017 © The University of Sheffield 34
  35. 35. 26/10/2017 © The University of Sheffield 35 Retrieve and/or Import data from a number of platforms
  36. 36. 26/10/2017 © The University of Sheffield 36 Exciting Development • DiscoverText is beta testing the ability to export to NodeXL. • The new functionality that creates GraphML files based on Twitter data.
  37. 37. 26/10/2017 © The University of Sheffield 37 • 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. 38. #WorldMentalHealthDay
  39. 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. 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. 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. 42. NodeXL Graph Gallery http://nodexlgraphgallery.org
  43. 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. 26/10/2017 © The University of Sheffield 43
  44. 44. 26/10/2017 © The University of Sheffield 44 Twitter as a Media Monitoring Tool
  45. 45. 26/10/2017 © The University of Sheffield 45 Monitoring Keywords
  46. 46. 26/10/2017 © The University of Sheffield 46 Reason for the Peak
  47. 47. 26/10/2017 © The University of Sheffield 47 Crisis Alerts using Visibrain
  48. 48. 26/10/2017 © The University of Sheffield 48 Social Media Analytics for Consumer Engagement
  49. 49. 26/10/2017 © The University of Sheffield 49 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.
  50. 50. 26/10/2017 © The University of Sheffield 50 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.
  51. 51. 26/10/2017 © The University of Sheffield 51 Filter and Target in Audience There are a number of methods of filtering and searching all Twitter users
  52. 52. 26/10/2017 © The University of Sheffield 52
  53. 53. 26/10/2017 © The University of Sheffield 53
  54. 54. 26/10/2017 © The University of Sheffield 54 Social Media For Security – Mon Locations
  55. 55. 26/10/2017 © The University of Sheffield 55 Social Media For Security – Monitoring Locations
  56. 56. 26/10/2017 © The University of Sheffield 56 Social Elephants
  57. 57. 26/10/2017 © The University of Sheffield 57 Social Elephants
  58. 58. Examine Reach 26/10/2017 © The University of Sheffield 58
  59. 59. 26/10/2017 © The University of Sheffield 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.
  60. 60. 26/10/2017 © The University of Sheffield Chorus Layout Keywords to retrieve data are entered here
  61. 61. Chorus Tweet Vis Layout 26/10/2017 © The University of Sheffield
  62. 62. 26/10/2017 © The University of Sheffield 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. 63. Mozdeh Layout 26/10/2017 © The University of Sheffield • An example time series graph of 5,055,299 tweets related to norovirus Enter keywords to search Search Feature Results
  64. 64. 26/10/2017 © The University of Sheffield 64 Time Series Graphs in Mozdeh
  65. 65. 26/10/2017 © The University of Sheffield 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.
  66. 66. 26/10/2017 © The University of Sheffield 66Twitter Archiving Google Sheets (TAGS)
  67. 67. 26/10/2017 © The University of Sheffield Twitter Archiving Google Sheet (TAGS)
  68. 68. 26/10/2017 © The University of Sheffield 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.
  69. 69. 26/10/2017 © The University of Sheffield COSMOS Project User Interface
  70. 70. 26/10/2017 © The University of Sheffield 70
  71. 71. 26/10/2017 © The University of Sheffield 71 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.
  72. 72. 26/10/2017 © The University of Sheffield 72 Social Network Analysis
  73. 73. 26/10/2017 © The University of Sheffield 73 Netlytic Reports
  74. 74. 26/10/2017 © The University of Sheffield 74 Over 3 Billion Images Shared On Social Media Every Day
  75. 75. 26/10/2017 © The University of Sheffield 75 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.
  76. 76. 26/10/2017 © The University of Sheffield1ju 76 Webometric Analyst
  77. 77. 26/10/2017 © The University of Sheffield 77 Netra Systems
  78. 78. 26/10/2017 © The University of Sheffield 78 Netra Systems
  79. 79. 26/10/2017 © The University of Sheffield 79 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.
  80. 80. 26/10/2017 © The University of Sheffield 80 There are many other tools • Crimson Hexagon • Brandwatch • Social Bakers • Simply Measured • Talk Walker • Sprout Social
  81. 81. 26/10/2017 © The University of Sheffield 81 Other Trends • Virtual Reality (Facebook’s Oculus technology) • Online Dating • Social Shopping (buy buttons)
  82. 82. 26/10/2017 © The University of Sheffield 82 Other Trends • Mobile Wallets • Live Streaming • Predictive Analytics
  83. 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. 26/10/2017 © The University of Sheffield 83
  84. 84. 26/10/2017 © The University of Sheffield 84 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. 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. 26/10/2017 © The University of Sheffield 85
  86. 86. 26/10/2017 © The University of Sheffield 86 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. 87. Questions?
  88. 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. 26/10/2017 © The University of Sheffield 88

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