Gmail Meter: Hacked with Neo4j
Rik Van Bruggen, Regional Sales Director of Neo Technology and data-mining explorer, recently donated some of his time to transform Gmail Meter statistics into incredible social graphs. Enjoy.
A bunch of different people have pointed out to me in recent months that there is something funny about Graphs and email analysis. From my work in previous years at security companies, I know that Email Forensics is actually big business. Figuring out who emails whom, about what topics, with what frequency, at what times – is important. Especially when the proverbial sh*t hits the fan and fraud comes to light – like in the Enron case. How do I get insight into email traffic? How do I know what was communicated to who? And how do I get that insight, without spending a true fortune?
So a couple of weeks ago I came across an article or two that featured Gmail Meter. This is of course not one of these powerful enterprise-ready forensics tools, but it is interesting. Written by Romain Vialard and Shuttlecloud, it provides you with a really straightforward way to get all kinds of stats and data about your use of Gmail.
In this blog post, we’ll take a look at how we can actually use Romain’s output, and generate a neo4j database that will allow us to visually and graphically explore the email traffic in our Gmail inboxes – without doing doing any coding.
Using Gmail Meter to create the dataset
The first thing you need to do to get going is to get Gmail Meter installed. Note: For this functionality I installed Gmail Meter manually, which involves searching through the Google Apps Script Gallery. If you’ve installed Gmail Meter via the website, you won’t have access to this data.
After installing Gmail Meter, a Google Doc spreadsheet populates in your Google Drive folder. This spreadsheet has two tabs:
Sheet1 – contains information about which email addresses you have been exchanging with, and how many emails you have been exchanging with them (sending and receiving)
Sheet2 – contains more information about the conversations, number of words, etc.
Now all we need to do is create a neo4j database based on this data – and that’s a piece of cake.