Twitter network analysis with Gephi | Part 2

To continue the previous Twitter network analysis with Reaktor, I downloaded more data from Twitter. This time with a python script called twecoll, which produces a Gephi compatible graph file. Using this script requires setting up a Twitter app, since the script uses your app’s consumer key and secret. Running twecoll script is very straightforward:

  1. twecoll init [twitter handle]  → Generates .dat-file with account details data (friends, followers, avatar URL, etc. for account friends), populates an img directory with avatar images.
  2. twecoll fetch [twitter handle]  populates the fdat directory, contains .f-files with list of friends of a user
  3. twecoll edgelist [twitter handle]  .gml-file with nodes and edges

The script took around 24 hours to scrape ReaktorNow‘s first and second degree relationships and their details. With Gephi the modularity of the nodes was calculated and modularity class was used to color nodes. Then nodes were resized by their degree and nodes with degree smaller than 15 were filtered out. Finally ForceAtlas2 algorithm was used for layout. As a result, the graph looked like this:

ReaktorNow graph

However, exploring such a large and complex graph is rather inconvenient with Gephi. By using SigmaExporter plugin, the graph can be exported as an interactive HTML5 webpage . The representation allows user to inspect modularity groups separately, search user by their Twitter handle and check detailed information of each node.

group2reaktor_detailsgroup_members

The explorable graph can be found here: https://g00g0l.github.io/g00g0l.github.io/#

By taking a closer look at the groups and their key influencers (by in-degree), some characteristics can be found.

Group 1 (green) – 344 members

A lot of active users of Twitter and media. Digitalists.

NameURLIndegree Followers Tweets
alfrehnhttps://twitter.com/alfrehn5125162411676
MikaelJungnerhttps://twitter.com/MikaelJungner436905869569
tuijahttps://twitter.com/tuija3772118920475
villetolvanenhttps://twitter.com/villetolvanen3732599866036
HenkkaHypponenhttps://twitter.com/HenkkaHypponen334858934213

Group 2 (purple) – 495 Members

More techy group, with Reaktor, Futurice and Mikko Hyppönen of F-Secure.

NameurlIndegreeFollowersTweets
ReaktorNowhttps://twitter.com/ReaktorNow126692826277
mikkohttps://twitter.com/mikko63314017034840
futuricehttps://twitter.com/futurice57872374416
SamiHonkonenhttps://twitter.com/SamiHonkonen51634487046
ReaktorBPhttps://twitter.com/ReaktorBP3719931086

Group 3 (blue) – 234 Members

International, techy and entrepreneurial. Bubbling under: Techcrunch, WIRED, Supercell, Nasa and SpaceX

NameurlIndegreeFollowersTweets
SlushHQhttps://twitter.com/SlushHQ457391086879
elonmuskhttps://twitter.com/elonmusk42261862462395
wilihttps://twitter.com/wili409509751426
aaltoeshttps://twitter.com/aaltoes390126514735
ipaananenhttps://twitter.com/ipaananen372289902640

Group 4 (orange) – 99 members

Based on the top profiles, many people with connections/interest to design. Many Reaktor employees and other developers.

NameurlIndegreeFollowersTweets
johanneskoskihttps://twitter.com/johanneskoski2639743787
aokhttps://twitter.com/aok21410551210
Mediumhttps://twitter.com/Medium201195465316052
tuomashttps://twitter.com/tuomas1965831087
teppohttps://twitter.com/teppo174180411162

Group 5 (red) – 45 members

Very Finnish group with IT people, the network is scattered.

NameurlIndegreeFollowersTweets
SarasvuoJarihttps://twitter.com/SarasvuoJari43213791126998
OtsoKivekashttps://twitter.com/OtsoKivekas12937261256
PauliKulhohttps://twitter.com/PauliKulho8913092
Schwarzeneggerhttps://twitter.com/Schwarzenegger8539848615182
salantsahttps://twitter.com/salantsa824262569

Group 6 (green)

Maybe a bit more business / leadership oriented network – also scattered

NameurlIndegreeFollowersTweets
NBForumHQhttps://twitter.com/NBForumHQ14572313245
GuyKawasakihttps://twitter.com/GuyKawasaki1211490407163217
ossilindrooshttps://twitter.com/ossilindroos1039561574
annarautiainenhttps://twitter.com/annarautiainen857002198
ariannahuffhttps://twitter.com/ariannahuff84256706033510

Analysing the networks became more and more difficult when moving from group 1 to group 6. The first groups are more tight and more connected within the group. The latter groups are more loosely connected and therefore hard to classify.

Network table in CSV form can be found here: reaktor_data

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