An overview of Twitter analytics
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The document outlines a workshop on Twitter analytics, presented by PhD student Wasim Ahmed, focusing on the platform's role during infectious disease outbreaks and the various analytics tools available. Key sections include an overview of Twitter's usage statistics, different types of APIs for data access, and case studies exemplifying brand management on Twitter. It emphasizes the importance of understanding social media data and the necessity of using multiple analytics tools to gain comprehensive insights.
An overview of Twitter analytics
- 1. An overview of Twitter analytics Wasim Ahmed (wahmed1@sheffield.ac.uk) (Twitter: @was3210) Acknowledgements to Sergej Lugovic (@sergejlugovic) Contemporary Issues in Economy & Technology (CIET). 15th June 2016. Split, Croatia.
- 2. 19/06/2016 © The University of Sheffield 2 About me • Second Year PhD student from the Information School, University of Sheffield (UK). • PhD examines content that is shared on Twitter during infectious disease outbreaks. • Run a social media research blog (over 11 thousand hits)
- 3. 19/06/2016 © The University of Sheffield 3 About me • Currently working on a PhD project examining infectious disease outbreaks on Twitter • Alongside PhD assisted security research teams, government, media, and educational organisations globally
- 4. About me…continued • Also work part time as a Research Associate: Social Media specialist 19/06/2016 © The University of Sheffield 4
- 5. 19/06/2016 © The University of Sheffield 5 Overview of workshop • Part 1 – Overview of Twitter, and case studies examples • Part 2 – Overview of Twitter analytics software / interactive sessions • Part 3 – Q&A on tools – make sure to jot down some questions!
- 6. 19/06/2016 © The University of Sheffield 6 Aims • Better understand Twitter as a platform • Provide examples of case studies using social media analytics • Gain knowledge and awareness of Twitter analytics
- 7. Twitter • Twitter allows brief <140 character text updates, known as ‘tweets’, to be shared with other users • Tweets can contain thoughts, feelings, activities, and opinions (Chew and Eysenbach, 2010). 19/06/2016 © The University of Sheffield 7
- 8. Twitter • Twitter reports having 316 million monthly active users • There being 500 million tweets per day • 80% of active Twitter users using a mobile device (About Twitter, n.d.). 19/06/2016 © The University of Sheffield 8
- 9. Why Twitter (data)? • See my LSE impact blog post baseline comparison to Facebook • Twitter is a popular platform in terms of the media attention it receives and it therefore attracts more research due to its cultural status • Twitter makes it easier to find and follow conversations (i.e., by both its search feature and by tweets appearing in Google search results) • Twitter has hashtag norms which make it easier gathering, sorting, and expanding searches when collecting data • Twitter data is easy to retrieve as major incidents, news stories and events on Twitter tend to be centred around a hashtag • The Twitter API is more open and accessible compared to other social media platforms, which makes Twitter more favourable to developers creating tools to access data. This consequently increases the availability of tools to researchers. • Many researchers themselves are using Twitter and because of their favourable personal experiences, they feel more comfortable with researching a familiar platform. 19/06/2016 © The University of Sheffield
- 10. Different types of Twitter API • Application Programming Interface • Twitter’s Search API – focused on relevance and not completeness, some tweets and users may be missing from results (7 days back in time up to 3200 queries) • Twitter Streaming API – The Streaming APIs give developers low latency access to Twitter’s global stream of tweet data (live stream) • Firehose API – in theory, 100% of Twitter data (most software allows up to 30 days worth of historical tweets) 19/06/2016 © The University of Sheffield
- 11. What if I want data going back more than 30 days? • In most instance you will have to pay for it • I use Texifter (@texifter) with DiscoverText (@discovertext) • Can range from not that expensive to very expensive depending on query and time 19/06/2016 © The University of Sheffield
- 12. Legal issues • Sharing of Twitter datasets is prohibited see https://dev.twitter.com/terms/api-terms • However, sharing Tweet IDs (to look up the tweets used is permissible). This is useful for reproducibility. 19/06/2016 © The University of Sheffield
- 13. 19/06/2016 © The University of Sheffield 13 Business Expenditure • Businesses spend millions of dollars every year tailoring their brands and protecting them • Historically traditional media and one-to- many approach gave control to brands via advertisers
- 14. Shift of Power 19/06/2016 © The University of Sheffield 14 • With emergence of social media the traditional brand communication process has reached something of a crisis • Traditional communication lines are rapidly breaking down
- 15. 19/06/2016 © The University of Sheffield 15 Shift of Power • When it became clear that Twitter was becoming an important social networking site and public communication platform • A number of businesses and social media marketing professionals attempted to exploit the platform for commercial purposes
- 16. Toyota • Toyota had to recall a number of its cars in 2009 ad 2010 due to a serious safety faulty which resulted in the deaths of over 50 people • Unlike Sony - they immediately went into Damage Control 19/06/2016 © The University of Sheffield 16
- 17. • As soon as the recall crisis start getting media attention Toyota quickly put together an ‘Online Newsroom’ and a ‘Social Media Strategy Team’ to coordinate all the media releases 19/06/2016 © The University of Sheffield 17 Toyota
- 18. Sony PlayStation Network • In mid-April 2011 the Playstation Network was suddenly shut down without explanation • Frustrations quickly spread through social media sites such as Twitter as gamers around the world voiced their annoyance at not being able to access their online games 19/06/2016 © The University of Sheffield 18
- 19. Sony PlayStation Network • The lack of regular updates and information from Sony served to incense users • Users struggled to determine what was fact and what was rumour on Twitter 19/06/2016 © The University of Sheffield 19
- 20. Sony PlayStation Network • Lapse in communication was incomprehensive to consumers • Lack of regular updates and information only served to incense users further 19/06/2016 © The University of Sheffield 20
- 21. • “I think It is pretty disgusting that Sony have waiting 7 days to tell users that their Credit Card details may have been compromised”. • “I bet the hacker will get emails out quicker than Sony!” 19/06/2016 © The University of Sheffield 21 Sony PlayStation Network
- 22. Toyota • While there was still anger and negative viewpoints shared through social media services, • Company was able to minimise their impact by eliminating confusion and keeping the consumer base regularly informed of developments 19/06/2016 © The University of Sheffield 22
- 23. Brand Management • The two cases have highlighted brands need to know how they are being mentioned across social media profiles • Social Media Analytics is now a huge market 19/06/2016 © The University of Sheffield 23
- 24. Types of analysis possible • Sentiment analysis has the potential to work well with Twitter data, as tweets are consistent in length (i.e., <= 140) • However sarcasm is difficult to detect within tweets. • SentiStrength algorithm (http://sentistrength.wlv.ac.uk/) 19/06/2016 © The University of Sheffield 24
- 25. Types of analysis possible • Time series analysis is normally used when examining tweets overtime to see when a peak of tweets may occur. One I made today: 19/06/2016 © The University of Sheffield 25
- 26. Last 30 days time series graph of Croatia 19/06/2016 © The University of Sheffield 26
- 27. Context behind the peak June 12th 2016 19/06/2016 © The University of Sheffield 27 Euro championship, Croatia win their opening game:
- 28. Types of analysis possible • Network analysis is used to visualize the connections between people (who is connected to who?) • Who is the most influential Twitter user? Various algorithms can be used, a popular algorithm is the Betweenness Centrality Algorithm 19/06/2016 © The University of Sheffield 28
- 29. Types of analysis possible • Network analysis is used to visualize the connections between people (who is connected to who?) • Who is the most influential Twitter user? Various algorithms can be used, a popular algorithm is the Betweenness Centrality Algorithm 19/06/2016 © The University of Sheffield 29
- 30. Betweenness Centrality Algorithm 19/06/2016 © The University of Sheffield 30 Image from / read more here http://med.bioinf.mpi- inf.mpg.de/netanalyzer/help/2.7/
- 31. Types of analysis possible • Machine Learning e.g. using a text classifier such as the naive Bayes algorithm • Involves training data e.g. manually coding a subset of data e.g, 100 tweets in a dataset of a 1,000 tweets and the algorithm will automatically classifier the remaining data 19/06/2016 © The University of Sheffield 31
- 32. Part 2 of the workshop • Part 2 of the workshop will provide an overview of some of the cutting edge analytics platforms out there • Pause here and create a Twitter account (if you don’t have one) 19/06/2016 © The University of Sheffield 32
- 33. Visibrain Focus (commercial) 19/06/2016 © The University of Sheffield 33
- 34. Visibrain Focus • Unfortunately not possible to get access for delegates • However, Visibrain offer a free 30 day trial • I can provide an overview on this machine 19/06/2016 © The University of Sheffield 34
- 35. Echosec (fee version available) 19/06/2016 © The University of Sheffield 35
- 36. Echosec (fee version available) • Location based social media search by location rather than keywords • Allows you to examine a specific geographical area by drawing on Facebook, Twitter, Instagram, Sina Weibo, Youtube, Foursquare, Flickr, and VK APIs 19/06/2016 © The University of Sheffield 36
- 37. 19/06/2016 © The University of Sheffield 37 Examples of case studies using Echosec • Echosec was used following the April 2015 Nepal Earthquake • Apps such as four-square have potential to provide first responders ability to check where things are • Geographically searching social media data in an area can show you what you are looking for in an emergency • Can examine locations of affected areas and see where people have stopped posting from
- 38. 19/06/2016 © The University of Sheffield 38 Real Examples of case studies using Echosec
- 39. Echosec • Navigate to https://app.echosec.net/ • Near the bottom left there will be an option to enter a location to search for • See what intelligence you can gain using location based search. (5-10 minutes) 19/06/2016 © The University of Sheffield 39
- 40. Follow the Hashtag • Free version available to access • Navigate to http://www.followthehashtag.com/ 19/06/2016 © The University of Sheffield 40
- 41. Twitonomy 19/06/2016 © The University of Sheffield 41
- 42. Twitonomy • Free version available to access navigate to: https://www.twitonomy.com/ 19/06/2016 © The University of Sheffield 42
- 43. NodeXL • Social media analysis that looks at the structure of the networks when using social media • One particular tool is called NodeXL, unfortunately not enough time to download and install, but can demonstrate on this machine 19/06/2016 © The University of Sheffield 43
- 44. NodeXL 19/06/2016 © The University of Sheffield 44 • To examine network graphs currently being created and uploaded. • Navigate to the NodeXL graph gallery http://www.nodexlgraphgallery.org/
- 45. NodeXL – Graph Gallery 19/06/2016 © The University of Sheffield 45
- 46. NodeXL 19/06/2016 © The University of Sheffield 46 • Example graphs on the Gallery • For interpretation see Smith, Rainie, Shneiderman, & Himelboim (2014) • Also see this example of 6 types of network graph
- 47. University of Sheffield Project 19/06/2016 © The University of Sheffield 47 • Produced a report for the Head of Digital at the University of Sheffield Stephen Thompson examining mentions of the University over previous 12 months
- 48. University of Sheffield Project • Step 1 – Obtain historical data using a provider such as Sifter and data placed into DiscoverText • Step 2 – Using DiscoverText de-duplicate data by removing exact duplicates, and near duplicate clusters 19/06/2016 © The University of Sheffield 48
- 49. University of Sheffield Project • Step 3 – Of a reduced dataset take a 10% sample and manually code/ and or train a machine classifier to code the entire dataset. • I used DiscoverText which is a cloud- based, collaborative text analytics solution, and which allows the above. 19/06/2016 © The University of Sheffield 49
- 50. DiscoverText 19/06/2016 © The University of Sheffield 50
- 51. University of Sheffield Project 19/06/2016 © The University of Sheffield 51 • By removing duplicates and near duplicates the sample of N=43,521 tweets became a total of N=13,078 tweets. • Prevents from categorizing only popular mentions.
- 52. University of Sheffield Project 19/06/2016 © The University of Sheffield 52 • A 10% random sample of tweets were extracted from the filtered dataset (i.e., 10% of 13,078) to leave a total of n=1,198 tweets (total coding time 19 hours 29 minutes and 20 seconds).
- 53. University of Sheffield Project 19/06/2016 © The University of Sheffield 53 • Conclusions and key findings: • A university that is very well engaged with its students, the public, and the mainstream media • Ranked highly amongst other Russell Group universities for followers, and mentions
- 54. Conclusion 19/06/2016 © The University of Sheffield 54 • There is no ‘best’ social media analytics tool as they all offer something different and I use them in combination
- 55. Questions? • Happy to answer any specific questions 19/06/2016 © The University of Sheffield 55