Downloaded 12 times
![[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](https://image.slidesharecdn.com/workshop-practicaltoolsoverview30may-170530182726/85/Social-Media-A-Practical-Approach-26-320.jpg)
![[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](https://image.slidesharecdn.com/workshop-practicaltoolsoverview30may-170530182726/85/Social-Media-A-Practical-Approach-27-320.jpg)
Uploaded byDr Wasim Ahmed
Social Media: A Practical Approach
The document provides an overview of social media research, focusing on the use of Twitter for academic purposes. It highlights various tools for capturing and analyzing Twitter data, such as DiscoverText, Mozdeh, and NodeXL, along with their respective features and pricing. Additionally, it discusses the potential applications of social media analytics in various fields and emphasizes the importance of social network analysis for understanding user interactions.
Social Media: A Practical Approach
- 1. Social Media: A PracticalApproach 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. About me • ThirdYear 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. 30/05/2017 © TheUniversity 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. 30/05/2017 © TheUniversity 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. Twitter as aConsumer 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. 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. 30/05/2017 © TheUniversity 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. 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. Twitter API • Twitter’sSearch 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. How do youretrieve 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. Types of Analysis •Content Analysis • Thematic Analysis • Network Analysis • Machine Learning • Sentiment Analysis 30/05/2017 © The University of Sheffield 11
- 12. 30/05/2017 © TheUniversity of Sheffield Tools Covered in this Presentation • DiscoverText • NodeXL • Chorus • Mozdeh • TAGS • COSMOS
- 13. DiscoverText 30/05/2017 © TheUniversity 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. DiscoverText used in… •Consumer industries • Education • Human Resources • Legal • Medical & Pharma • Government • Military 30/05/2017 © The University of Sheffield 14
- 15. DiscoverText as DataScience • 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. Fiver Pillars ofText Analytics • Search • Filtering • De-duplication and Clustering • Human Coding • Machine-Learning 30/05/2017 © The University of Sheffield 16
- 17. For a topicoverview 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. Filtering Data 30/05/2017 ©The University of Sheffield 18
- 19. DiscoverText has ActiveLearning • 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. An example: ManchesterDerby • 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. 30/05/2017 © TheUniversity of Sheffield 21 Import Twitter data
- 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. Importance of GeneratingClusters 30/05/2017 © The University of Sheffield 23
- 24.
- 25. • Most frequentlyshared 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. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] BrandClusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter networks
- 27. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] BrandClusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter networks
- 28. 30/05/2017 © TheUniversity 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. Betweenness Centrality From RichardIngram’s blog post visualising Data: Seeing is Believing http://www.richardingram.co.uk/2012/12/visu alising-data-seeing-is-believing/
- 30. Degree Centrality From RichardIngram’s blog post visualising Data: Seeing is Believing http://www.richardingram.co.uk/2012/12/visu alising-data-seeing-is-believing/
- 31. 30/05/2017 © TheUniversity of Sheffield 31 Theresa May (29th May)
- 32. 30/05/2017 © TheUniversity 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. 30/05/2017 © TheUniversity of Sheffield Chorus • This is the layout of Chorus Tweet Catcher
- 34. Chorus • This isthe layout of Chorus Tweet Vis 30/05/2017 © The University of Sheffield
- 35. Chorus Tutorials • Chorusmanual here • Great video overview of Chorus here 30/05/2017 © The University of Sheffield
- 36. 30/05/2017 © TheUniversity 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. Mozdeh 30/05/2017 © TheUniversity of Sheffield • An example time series graph of 5,055,299 tweets related to norovirus
- 38. 30/05/2017 © TheUniversity of Sheffield 38 Time Series Graphs
- 39. Mozdeh Tutorials • Greatuser guide here • Great theoretical overview here 30/05/2017 © The University of Sheffield
- 40. 30/05/2017 © TheUniversity 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. 30/05/2017 © TheUniversity of Sheffield 41
- 42. 30/05/2017 © TheUniversity of Sheffield TAGS – Twitter Archiving Google Sheet
- 43. 30/05/2017 © TheUniversity 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. 30/05/2017 © TheUniversity of Sheffield COSMOS Project • Some of the features include generating: • Word Clouds • Frequency Charts • Network Graphs • Geographical Maps of Tweets
- 45. 30/05/2017 © TheUniversity of Sheffield COSMOS Project Layout
- 46. 30/05/2017 © TheUniversity of Sheffield COSMOS Tutorials • Great video tutorial(s) here
- 47. NVivo • You canimport 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. Summary • This presentationhas 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. 30/05/2017 © TheUniversity 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. 30/05/2017 © TheUniversity 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. iConference 2018 inSheffield • 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. 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.