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

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Presentation by Beth Granter from Brilliant Noise at the second Big Data Brighton meetup, hosted by Brandwatch: www.brandwatch.com

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

1. 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.comThursday, 29 November 12
2. 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
4. 4. twitter.com/who_to_follow/importThursday, 29 November 12
5. 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. 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 @bethgranterThursday, 29 November 12
7. 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. 8. Outputs: accounts by network follower count (network popularity)Thursday, 29 November 12
9. 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 metricThursday, 29 November 12
10. 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. 11. Outputs: accounts by network inﬂuence/relevanceThursday, 29 November 12
12. 12. Outputs: @bethgranter follower data via Followerwonk.comThursday, 29 November 12
13. 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. 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. 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. 16. Detail of methodThursday, 29 November 12
17. 17. Getting the Twitter user IDs for the two tier network import CSVThursday, 29 November 12
18. 18. Getting the Twitter user IDs for the two tier network import CSVThursday, 29 November 12
19. 19. Google reﬁne - from list of network follower Twitter user ids & network follower count import CSVThursday, 29 November 12
20. 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. 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 requiredThursday, 29 November 12
22. 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. 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. 24. Thanks to NixonMcInnes! Brilliant Noise November 2012 @bethgranter bethgranter.com @brilliantnoise brilliantnoise.comThursday, 29 November 12