SlideShare uma empresa Scribd logo
1 de 24
Baixar para ler offline
Mapping influencers by
                           network connections with
                           Google Refine

                           Brilliant Noise - case study
                           thanks to NixonMcInnes
                           Beth Granter
                           November 2012

                           @bethgranter
                           bethgranter.com

                           @brilliantnoise
                           brilliantnoise.com




Thursday, 29 November 12
Background and brief


         The client engages with individuals via an email list
         in an internal database, and a LinkedIn group.

         A client spokesperson is one of the ‘faces’ of the
         department with a keen following on Twitter via
         his personal account e.g. @bethgranter (!)

         The brief was to look at the people in the three
         groups & use that insight to create a list of similar
         influencers they should be engaging with.




Thursday, 29 November 12
Approach
         LinkedIn group: LinkedIn API and terms and
         conditions -exporting member names or any
         details from the group not legal... no further action!

         Email list: created a temporary Gmail account &
         added users as contacts, then used a temporary
         Twitter account, imported (via Gmail) contacts into
         Twitter, copied list of matching Twitter accounts to
         spreadsheet.

         Output: list of 95 Twitter accounts w/ full details,
         who we know also receive the clients’ emails.

         Data stored in shared Google Docs spreadsheet.




Thursday, 29 November 12
twitter.com/who_to_follow/import




Thursday, 29 November 12
Approach: Twitter network
         @bethgranter’s followers: exported a list of all of
         followers via Twitter API, and again using the Twitter
         API gathered a list of everybody else they follow.

         This gave us a niche, ‘two tier network’ of ~600,000
         people.

         We then calculated a unique index - a ‘network
         follower count’ - by calculating how many of
         @bethgranter’s followers follow each person in the
         network. This gave us a popularity figure.

         Overall there were over 1 million connections mapped.




Thursday, 29 November 12
The network
                                   Network follower count

                                   A = 0 not followed by
                           A
                           A       anyone in the network
                               C
                               C
                                   B = 2 followed by 2 other
                      B
                      B
                                   followers of @bethgranter
                               D
                               D

                                   C = 1 followed by 1 of
                                   @bethgranter’s followers

                                   D = 2 followed by 2 other
                                   followers of @bethgranter




Thursday, 29 November 12
Detail: method to get network
        - Use Twitter API to get all followers of @bethgranter
          = level 1 network follower

        - For each level 1 network follower, get everyone else they follow
          = level 2 network follower

        - For everyone in level 1 & level 2, count how many level 1 followers they
          have (we don’t know who level 2 follows).
          = network follower count




        - Twitter API limits rate of calls to do this...




Thursday, 29 November 12
Outputs: accounts by network follower count
         (network popularity)




Thursday, 29 November 12
Approach: network influence/relevance
         Filtered list to top 500 ppl by total follower count, so
         only looking at ppl w/ minimum of ~250 followers total.

         Calculate potential ‘influence’ figure for members in the
         network: proportion of each person’s total followers
         who were also followers of @bethgranter, i.e. their
         network follower count as a percentage of their total
         follower count.

         = likelihood that a person’s follower chosen at random
         is also following @bethgranter. i.e. how relevant are
         their followers? We can use this figure as a network
         influence/relevance metric




Thursday, 29 November 12
Approach: network influence/relevance
                           % network follows vs total follows

                           @guardianeco is followed by 428 of
                           @bethgranter’s followers and 98933
                           people in total, so network influence =
                           0.43% (low)

                           @Siemens_Energy follows @bethgranter,
                           is followed by 101 of @bethgranter’s
                           followers and 32008 in total, so network
                           influence = 0.32% (low)

                           @SDStephDraper is followed by 73 of
                           @bethgranter’s followers and 269 in total,
                           so network influence = 27.14% (high)




Thursday, 29 November 12
Outputs: accounts by network influence/relevance




Thursday, 29 November 12
Outputs: @bethgranter follower data via
         Followerwonk.com




Thursday, 29 November 12
Summary of project outputs
         List of 96 Twitter accounts w/ full details, which we know are also
         subscribed to client’s email newsletter

         List of 500 Twitter accounts in a newly mapped network, people
         within two steps of @bethgranter which can be sorted by:

         - overall popularity (total followers)

         - network popularity (network followers) or by

         - network influence/relevance (% network follows vs total follows)

         Demographic and bio data about @bethgranter’s followers
         Sorting list by relevance or popularity can be used to achieve different objectives.
         Sorting by relevance identifies ppl who could amplify messages in the current
         network, sorting by popularity identifies ppl who can extend the reach of
         messages, although popular accounts will be harder to engage with.




Thursday, 29 November 12
Conclusions
         This project used innovative data analysis techniques to explore a
         bespoke network, based on relationships between people rather
         than focusing on self-defined spokespeople on a topic.

         The outputs of this project will only be effective if they are used by
         the client to achieve their goals through building relationships with
         the influencers identified.

         The client will then need a strategic approach to engaging with
         influencers online.




Thursday, 29 November 12
Next steps for the project
        Case studying the project & publishing some of its outputs online would
        attract the interest of those influencers we identified, and could therefore
        be used as a PR asset in itself.

        The approach could be re-applied to different spokespeople within and
        beyond the department, and to different email lists.

        Further research using the lists created in this project, such as:

        - investigating ‘hubs’ within the network (core groups)

        - creating an interactive visual map of the network as an asset

        - looking at overlaps between different lists, to identify gaps, e.g. looking
          at people on the email list who have a Twitter account, flagging
          whether or not they follow @bethgranter, and then tailoring outgoing
          comms with a relevant call to action (follow @bethgranter etc.)




Thursday, 29 November 12
Detail of method




Thursday, 29 November 12
Getting the Twitter user IDs for the two tier
         network




         import CSV




Thursday, 29 November 12
Getting the Twitter user IDs for the two tier
         network




         import CSV




Thursday, 29 November 12
Google refine - from list of network follower
         Twitter user ids & network follower count




         import CSV




Thursday, 29 November 12
Google refine - from list of network follower
         Twitter user ids & network follower count
                                     Create column
                                     based on
                                     twitter_user_id
                                     column by fetching
                                     URLs...




Thursday, 29 November 12
Google refine - from list of network follower
         Twitter user ids & network follower count
                                      Create column
                                      based on
                                      twitter_user_id
                                      column by fetching
                                      URLs...

                                      Use the Twitter API
                                      guide to get the
                                      URL for the data
                                      required




Thursday, 29 November 12
Google refine - from list of network follower
         Twitter user ids & network follower count
                                     Now you have the
                                     Twitter user data,
                                     you can separate it
                                     out...




Thursday, 29 November 12
Google refine - from list of network follower
         Twitter user ids & network follower count
                                      Then export to
                                      CSV / Google
                                      Docs / excel to
                                      sort & calculate
                                      influence metrics
                                      etc.




Thursday, 29 November 12
Thanks to NixonMcInnes!

                           Brilliant Noise
                           November 2012

                           @bethgranter
                           bethgranter.com

                           @brilliantnoise
                           brilliantnoise.com




Thursday, 29 November 12

Mais conteúdo relacionado

Semelhante a Mapping Influencers by Network Connections with Google Refine (Beth Granter, Brilliant Noise at Big Data Brighton)

Grid Social Networks Gada07
Grid Social Networks Gada07Grid Social Networks Gada07
Grid Social Networks Gada07guest078724
 
Twitter: Social Network Or News Medium?
Twitter: Social Network Or News Medium?Twitter: Social Network Or News Medium?
Twitter: Social Network Or News Medium?Serge Beckers
 
Twitter: Social Network Or News Medium?
Twitter: Social Network Or News Medium?Twitter: Social Network Or News Medium?
Twitter: Social Network Or News Medium?Serge Beckers
 
Social media tools for audience research and measurement and relevant influen...
Social media tools for audience research and measurement and relevant influen...Social media tools for audience research and measurement and relevant influen...
Social media tools for audience research and measurement and relevant influen...Brilliant Noise
 
IRJET - Twitter Spam Detection using Cobweb
IRJET - Twitter Spam Detection using CobwebIRJET - Twitter Spam Detection using Cobweb
IRJET - Twitter Spam Detection using CobwebIRJET Journal
 
Development of Twitter Application #5 - Users
Development of Twitter Application #5 - UsersDevelopment of Twitter Application #5 - Users
Development of Twitter Application #5 - UsersMyungjin Lee
 
Graph Based User Interest Modeling in Twitter
Graph Based User Interest Modeling in TwitterGraph Based User Interest Modeling in Twitter
Graph Based User Interest Modeling in Twitterraghavr186
 
DP1_160430723010_Divya.pptx
DP1_160430723010_Divya.pptxDP1_160430723010_Divya.pptx
DP1_160430723010_Divya.pptxDivyaPatel729457
 
Detection and Analysis of Twitter Trending Topics via Link-Anomaly Detection
Detection and Analysis of Twitter Trending Topics via Link-Anomaly DetectionDetection and Analysis of Twitter Trending Topics via Link-Anomaly Detection
Detection and Analysis of Twitter Trending Topics via Link-Anomaly DetectionIJERA Editor
 
NMIX 4200 Final Paper Report
NMIX 4200 Final Paper ReportNMIX 4200 Final Paper Report
NMIX 4200 Final Paper ReportPatrick Grant
 

Semelhante a Mapping Influencers by Network Connections with Google Refine (Beth Granter, Brilliant Noise at Big Data Brighton) (12)

Grid Social Networks Gada07
Grid Social Networks Gada07Grid Social Networks Gada07
Grid Social Networks Gada07
 
Twitter: Social Network Or News Medium?
Twitter: Social Network Or News Medium?Twitter: Social Network Or News Medium?
Twitter: Social Network Or News Medium?
 
Twitter: Social Network Or News Medium?
Twitter: Social Network Or News Medium?Twitter: Social Network Or News Medium?
Twitter: Social Network Or News Medium?
 
Social media tools for audience research and measurement and relevant influen...
Social media tools for audience research and measurement and relevant influen...Social media tools for audience research and measurement and relevant influen...
Social media tools for audience research and measurement and relevant influen...
 
Exploration of gaps in Bitly's spam detection and relevant countermeasures
Exploration of gaps in Bitly's spam detection and relevant countermeasuresExploration of gaps in Bitly's spam detection and relevant countermeasures
Exploration of gaps in Bitly's spam detection and relevant countermeasures
 
IRJET - Twitter Spam Detection using Cobweb
IRJET - Twitter Spam Detection using CobwebIRJET - Twitter Spam Detection using Cobweb
IRJET - Twitter Spam Detection using Cobweb
 
Development of Twitter Application #5 - Users
Development of Twitter Application #5 - UsersDevelopment of Twitter Application #5 - Users
Development of Twitter Application #5 - Users
 
Graph Based User Interest Modeling in Twitter
Graph Based User Interest Modeling in TwitterGraph Based User Interest Modeling in Twitter
Graph Based User Interest Modeling in Twitter
 
DP1_160430723010_Divya.pptx
DP1_160430723010_Divya.pptxDP1_160430723010_Divya.pptx
DP1_160430723010_Divya.pptx
 
Detection and Analysis of Twitter Trending Topics via Link-Anomaly Detection
Detection and Analysis of Twitter Trending Topics via Link-Anomaly DetectionDetection and Analysis of Twitter Trending Topics via Link-Anomaly Detection
Detection and Analysis of Twitter Trending Topics via Link-Anomaly Detection
 
SocialLda
SocialLda SocialLda
SocialLda
 
NMIX 4200 Final Paper Report
NMIX 4200 Final Paper ReportNMIX 4200 Final Paper Report
NMIX 4200 Final Paper Report
 

Mais de Brandwatch

Identifying and Analyzing a target audience with Analytics
Identifying and Analyzing a target audience with Analytics Identifying and Analyzing a target audience with Analytics
Identifying and Analyzing a target audience with Analytics Brandwatch
 
Brand protection & Crisis Aversion
Brand protection & Crisis AversionBrand protection & Crisis Aversion
Brand protection & Crisis AversionBrandwatch
 
Leveraging Insights with Creative Segmentation
Leveraging Insights with Creative SegmentationLeveraging Insights with Creative Segmentation
Leveraging Insights with Creative SegmentationBrandwatch
 
Life As a Brandwatch Analyst
Life As a Brandwatch AnalystLife As a Brandwatch Analyst
Life As a Brandwatch AnalystBrandwatch
 
Intelligence: The Fundamentals
Intelligence: The Fundamentals Intelligence: The Fundamentals
Intelligence: The Fundamentals Brandwatch
 
Control vs. Culture: The New Technology Operating Environment
Control vs. Culture: The New Technology Operating EnvironmentControl vs. Culture: The New Technology Operating Environment
Control vs. Culture: The New Technology Operating EnvironmentBrandwatch
 
Collective creativity for better intelligence
Collective creativity for better intelligenceCollective creativity for better intelligence
Collective creativity for better intelligenceBrandwatch
 
Ethics and humanity in the age of technology
Ethics and humanity in the age of technology Ethics and humanity in the age of technology
Ethics and humanity in the age of technology Brandwatch
 
Digital transformation in a regulated industry
Digital transformation in a regulated industry Digital transformation in a regulated industry
Digital transformation in a regulated industry Brandwatch
 
Emotional Intelligence
Emotional Intelligence Emotional Intelligence
Emotional Intelligence Brandwatch
 
25 things we learned analyzing billions of tweets
25 things we learned analyzing billions of tweets   25 things we learned analyzing billions of tweets
25 things we learned analyzing billions of tweets Brandwatch
 
PSB + Aga Khan Foundation: United We Brand
PSB + Aga Khan Foundation: United We BrandPSB + Aga Khan Foundation: United We Brand
PSB + Aga Khan Foundation: United We BrandBrandwatch
 
Ditch the Label and Brandwatch: Mental Health Study, 2017
Ditch the Label and Brandwatch: Mental Health Study, 2017Ditch the Label and Brandwatch: Mental Health Study, 2017
Ditch the Label and Brandwatch: Mental Health Study, 2017Brandwatch
 
Telling a story with your social insights
Telling a story with your social insightsTelling a story with your social insights
Telling a story with your social insightsBrandwatch
 
Combining Brandwatch and non Brandwatch data using Vizia 2
Combining Brandwatch and non Brandwatch data using Vizia 2Combining Brandwatch and non Brandwatch data using Vizia 2
Combining Brandwatch and non Brandwatch data using Vizia 2Brandwatch
 
How can social listening help to determine ROI?
How can social listening help to determine ROI?How can social listening help to determine ROI?
How can social listening help to determine ROI?Brandwatch
 
One step ahead: How Co-op uses Brandwatch to inform their business
One step ahead: How Co-op uses Brandwatch to inform their businessOne step ahead: How Co-op uses Brandwatch to inform their business
One step ahead: How Co-op uses Brandwatch to inform their businessBrandwatch
 
Today’s Reality: Managing & Monitoring Campus Crises through Social Media
Today’s Reality: Managing & Monitoring Campus Crises through Social MediaToday’s Reality: Managing & Monitoring Campus Crises through Social Media
Today’s Reality: Managing & Monitoring Campus Crises through Social MediaBrandwatch
 
Social Truth: Revealing what Truly Matters to Customers
Social Truth: Revealing what Truly Matters to CustomersSocial Truth: Revealing what Truly Matters to Customers
Social Truth: Revealing what Truly Matters to CustomersBrandwatch
 
Social Maturity
Social MaturitySocial Maturity
Social MaturityBrandwatch
 

Mais de Brandwatch (20)

Identifying and Analyzing a target audience with Analytics
Identifying and Analyzing a target audience with Analytics Identifying and Analyzing a target audience with Analytics
Identifying and Analyzing a target audience with Analytics
 
Brand protection & Crisis Aversion
Brand protection & Crisis AversionBrand protection & Crisis Aversion
Brand protection & Crisis Aversion
 
Leveraging Insights with Creative Segmentation
Leveraging Insights with Creative SegmentationLeveraging Insights with Creative Segmentation
Leveraging Insights with Creative Segmentation
 
Life As a Brandwatch Analyst
Life As a Brandwatch AnalystLife As a Brandwatch Analyst
Life As a Brandwatch Analyst
 
Intelligence: The Fundamentals
Intelligence: The Fundamentals Intelligence: The Fundamentals
Intelligence: The Fundamentals
 
Control vs. Culture: The New Technology Operating Environment
Control vs. Culture: The New Technology Operating EnvironmentControl vs. Culture: The New Technology Operating Environment
Control vs. Culture: The New Technology Operating Environment
 
Collective creativity for better intelligence
Collective creativity for better intelligenceCollective creativity for better intelligence
Collective creativity for better intelligence
 
Ethics and humanity in the age of technology
Ethics and humanity in the age of technology Ethics and humanity in the age of technology
Ethics and humanity in the age of technology
 
Digital transformation in a regulated industry
Digital transformation in a regulated industry Digital transformation in a regulated industry
Digital transformation in a regulated industry
 
Emotional Intelligence
Emotional Intelligence Emotional Intelligence
Emotional Intelligence
 
25 things we learned analyzing billions of tweets
25 things we learned analyzing billions of tweets   25 things we learned analyzing billions of tweets
25 things we learned analyzing billions of tweets
 
PSB + Aga Khan Foundation: United We Brand
PSB + Aga Khan Foundation: United We BrandPSB + Aga Khan Foundation: United We Brand
PSB + Aga Khan Foundation: United We Brand
 
Ditch the Label and Brandwatch: Mental Health Study, 2017
Ditch the Label and Brandwatch: Mental Health Study, 2017Ditch the Label and Brandwatch: Mental Health Study, 2017
Ditch the Label and Brandwatch: Mental Health Study, 2017
 
Telling a story with your social insights
Telling a story with your social insightsTelling a story with your social insights
Telling a story with your social insights
 
Combining Brandwatch and non Brandwatch data using Vizia 2
Combining Brandwatch and non Brandwatch data using Vizia 2Combining Brandwatch and non Brandwatch data using Vizia 2
Combining Brandwatch and non Brandwatch data using Vizia 2
 
How can social listening help to determine ROI?
How can social listening help to determine ROI?How can social listening help to determine ROI?
How can social listening help to determine ROI?
 
One step ahead: How Co-op uses Brandwatch to inform their business
One step ahead: How Co-op uses Brandwatch to inform their businessOne step ahead: How Co-op uses Brandwatch to inform their business
One step ahead: How Co-op uses Brandwatch to inform their business
 
Today’s Reality: Managing & Monitoring Campus Crises through Social Media
Today’s Reality: Managing & Monitoring Campus Crises through Social MediaToday’s Reality: Managing & Monitoring Campus Crises through Social Media
Today’s Reality: Managing & Monitoring Campus Crises through Social Media
 
Social Truth: Revealing what Truly Matters to Customers
Social Truth: Revealing what Truly Matters to CustomersSocial Truth: Revealing what Truly Matters to Customers
Social Truth: Revealing what Truly Matters to Customers
 
Social Maturity
Social MaturitySocial Maturity
Social Maturity
 

Mapping Influencers by Network Connections with Google Refine (Beth Granter, Brilliant Noise at Big Data Brighton)

  • 1. Mapping influencers by network connections with Google Refine Brilliant Noise - case study thanks to NixonMcInnes Beth Granter November 2012 @bethgranter bethgranter.com @brilliantnoise brilliantnoise.com Thursday, 29 November 12
  • 2. Background and brief The client engages with individuals via an email list in an internal database, and a LinkedIn group. A client spokesperson is one of the ‘faces’ of the department with a keen following on Twitter via his personal account e.g. @bethgranter (!) The brief was to look at the people in the three groups & use that insight to create a list of similar influencers they should be engaging with. Thursday, 29 November 12
  • 3. Approach LinkedIn group: LinkedIn API and terms and conditions -exporting member names or any details from the group not legal... no further action! Email list: created a temporary Gmail account & added users as contacts, then used a temporary Twitter account, imported (via Gmail) contacts into Twitter, copied list of matching Twitter accounts to spreadsheet. Output: list of 95 Twitter accounts w/ full details, who we know also receive the clients’ emails. Data stored in shared Google Docs spreadsheet. Thursday, 29 November 12
  • 5. Approach: Twitter network @bethgranter’s followers: exported a list of all of followers via Twitter API, and again using the Twitter API gathered a list of everybody else they follow. This gave us a niche, ‘two tier network’ of ~600,000 people. We then calculated a unique index - a ‘network follower count’ - by calculating how many of @bethgranter’s followers follow each person in the network. This gave us a popularity figure. Overall there were over 1 million connections mapped. Thursday, 29 November 12
  • 6. The network Network follower count A = 0 not followed by A A anyone in the network C C B = 2 followed by 2 other B B followers of @bethgranter D D C = 1 followed by 1 of @bethgranter’s followers D = 2 followed by 2 other followers of @bethgranter Thursday, 29 November 12
  • 7. Detail: method to get network - Use Twitter API to get all followers of @bethgranter = level 1 network follower - For each level 1 network follower, get everyone else they follow = level 2 network follower - For everyone in level 1 & level 2, count how many level 1 followers they have (we don’t know who level 2 follows). = network follower count - Twitter API limits rate of calls to do this... Thursday, 29 November 12
  • 8. Outputs: accounts by network follower count (network popularity) Thursday, 29 November 12
  • 9. Approach: network influence/relevance Filtered list to top 500 ppl by total follower count, so only looking at ppl w/ minimum of ~250 followers total. Calculate potential ‘influence’ figure for members in the network: proportion of each person’s total followers who were also followers of @bethgranter, i.e. their network follower count as a percentage of their total follower count. = likelihood that a person’s follower chosen at random is also following @bethgranter. i.e. how relevant are their followers? We can use this figure as a network influence/relevance metric Thursday, 29 November 12
  • 10. Approach: network influence/relevance % network follows vs total follows @guardianeco is followed by 428 of @bethgranter’s followers and 98933 people in total, so network influence = 0.43% (low) @Siemens_Energy follows @bethgranter, is followed by 101 of @bethgranter’s followers and 32008 in total, so network influence = 0.32% (low) @SDStephDraper is followed by 73 of @bethgranter’s followers and 269 in total, so network influence = 27.14% (high) Thursday, 29 November 12
  • 11. Outputs: accounts by network influence/relevance Thursday, 29 November 12
  • 12. Outputs: @bethgranter follower data via Followerwonk.com Thursday, 29 November 12
  • 13. Summary of project outputs List of 96 Twitter accounts w/ full details, which we know are also subscribed to client’s email newsletter List of 500 Twitter accounts in a newly mapped network, people within two steps of @bethgranter which can be sorted by: - overall popularity (total followers) - network popularity (network followers) or by - network influence/relevance (% network follows vs total follows) Demographic and bio data about @bethgranter’s followers Sorting list by relevance or popularity can be used to achieve different objectives. Sorting by relevance identifies ppl who could amplify messages in the current network, sorting by popularity identifies ppl who can extend the reach of messages, although popular accounts will be harder to engage with. Thursday, 29 November 12
  • 14. Conclusions This project used innovative data analysis techniques to explore a bespoke network, based on relationships between people rather than focusing on self-defined spokespeople on a topic. The outputs of this project will only be effective if they are used by the client to achieve their goals through building relationships with the influencers identified. The client will then need a strategic approach to engaging with influencers online. Thursday, 29 November 12
  • 15. Next steps for the project Case studying the project & publishing some of its outputs online would attract the interest of those influencers we identified, and could therefore be used as a PR asset in itself. The approach could be re-applied to different spokespeople within and beyond the department, and to different email lists. Further research using the lists created in this project, such as: - investigating ‘hubs’ within the network (core groups) - creating an interactive visual map of the network as an asset - looking at overlaps between different lists, to identify gaps, e.g. looking at people on the email list who have a Twitter account, flagging whether or not they follow @bethgranter, and then tailoring outgoing comms with a relevant call to action (follow @bethgranter etc.) Thursday, 29 November 12
  • 16. Detail of method Thursday, 29 November 12
  • 17. Getting the Twitter user IDs for the two tier network import CSV Thursday, 29 November 12
  • 18. Getting the Twitter user IDs for the two tier network import CSV Thursday, 29 November 12
  • 19. Google refine - from list of network follower Twitter user ids & network follower count import CSV Thursday, 29 November 12
  • 20. Google refine - from list of network follower Twitter user ids & network follower count Create column based on twitter_user_id column by fetching URLs... Thursday, 29 November 12
  • 21. Google refine - from list of network follower Twitter user ids & network follower count Create column based on twitter_user_id column by fetching URLs... Use the Twitter API guide to get the URL for the data required Thursday, 29 November 12
  • 22. Google refine - from list of network follower Twitter user ids & network follower count Now you have the Twitter user data, you can separate it out... Thursday, 29 November 12
  • 23. Google refine - from list of network follower Twitter user ids & network follower count Then export to CSV / Google Docs / excel to sort & calculate influence metrics etc. Thursday, 29 November 12
  • 24. Thanks to NixonMcInnes! Brilliant Noise November 2012 @bethgranter bethgranter.com @brilliantnoise brilliantnoise.com Thursday, 29 November 12