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

Mapping influencers by network connections with Google Refine

Brilliant Noise - case study thanks to NixonMcInnesBeth GranterNovember 2012

@bethgranterbethgranter.com

@brilliantnoisebrilliantnoise.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.


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.


twitter.com/who_to_follow/import


https://twitter.com/i/#!/who_to_follow/import

https://twitter.com/i/#!/who_to_follow/import

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.


The network

Network follower count

A = 0 not followed by anyone in the network

B = 2 followed by 2 other followers of @bethgranter

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

D = 2 followed by 2 other followers of @bethgranter

A

B

C

D

A

B

C

D


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...


Outputs: accounts by network follower count (network popularity)


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


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)


Outputs: accounts by network influence/relevance


Outputs: @bethgranter follower data via Followerwonk.com


Summary of project outputsList 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.


ConclusionsThis 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.


Next steps for the projectCase 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.)


Detail of method


Getting the Twitter user IDs for the two tier network

import CSV


Google refine - from list of network follower Twitter user ids & network follower count

import CSV



Create column based on twitter_user_id column by fetching URLs...



Create column based on twitter_user_id column by fetching URLs...

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



Now you have the Twitter user data, you can separate it out...



Then export to CSV / Google Docs / excel to sort & calculate influence metrics etc.


Thanks to NixonMcInnes!

Brilliant NoiseNovember 2012

@bethgranterbethgranter.com

@brilliantnoisebrilliantnoise.com


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

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