Skip to content

Latest commit

 

History

History
147 lines (93 loc) · 4.79 KB

README.md

File metadata and controls

147 lines (93 loc) · 4.79 KB
Raw

Google Group Message Processing

Summary

This process has several steps.

First, all messages from the group need to be downloaded locally.

Next, we iterated through these messages and processed them. For us, this meant discarding the HTML verions of the messages, and inserting the information we cared about into an SQLite database.

After that, we could query and filter them, and export the information we needed for a bulk mail to CSV.

Downloading

Messages were downloaded with a modification to this project: https://github.com/geniusgordon/go-google-group-crawler

Our fork of this project, allowing access to private groups and fixing a couple of bugs, can be found here: https://github.com/redguava/go-google-group-crawler

Preparation

Our fork expects a file named cookies.txt to exist in its directory to allow it access to your private group.

To create this file, visit your group in Chrome (while logged in), with the developer console open. In the network tab, find the main page request, right click it and select Copy as cURL (bash) from the context menu.

This curl request has a number of parameters, but the one we want is a header (-H) parameter beginning with cookie:. Copy this entire parameter after the word cookie: into your cookie.txt file, excluding the closing single quote (').

If you don't have it installed already, you'll need to install Go. Instructions can be found here: https://golang.org/doc/install

Let's get started

run:

go run crawler.go -o [YOUR_GOOGLE_GROUP_ORGANIZATION] -g [YOUR_GROUP_ID] -t 4

This will download all the messages from this group into a subdirectory.

First it will find and download the URLs of all of the threads in the group to <your group name/threads.
Next, it will find and download the URLs of all of the messages in the group to <your group name/msgs.
Finally, the messages themselves, in RFC 822 format, will be downloaded to <your group name>/mbox.

Depending on the size of your group, this can take hours (or longer), and occupy many gigabytes.

If these files are all you need, you can stop here.

Processing messages

We used the following project to import these output files into a static database: https://github.com/redguava/group-message-parser

Install Ruby and bundler, and the sqlite3 development packages, and run

bundle install

to install the project dependencies.

Next run:

bin/parser import --dir /path/to/your/mbox/directory --cutoffdate [CUTOFF_DATE] --ignore [IGNORE_EMAIL_DOMAIN]

Any messages sent before CUTOFF_DATE will be ignored
Any messages sent from IGNORE_EMAIL_DOMAIN will be ignored.

Example:

bin/parser import --dir ~/my_group/mbox --cutoffdate "20-03-2019" --ignore hotmail.com

Will import files from ~/my_group/mbox, ignoring any messages sent before March 20, 2019, and skip messages sent from the hotmail.com domain.

By default, files will be written to a sqlite file named messages.db.

Importing Intercom contacts

To be able to filter these messages based on whether the sending user was already in Intercom, we exported a CSV of our Intercom contacts, and imported these into a new table in the messages.db database file.

In Intercom, click the Platform sidebar button, and navigate to People > All users.

In the column list dropdown, deselect all attributes except for Email (some cannot be deselected), and from the More dropdown, choose Export users.

You will receive an email from Intercom when this export is ready to download. Place it in the group-message-parser directory, and run:

bin/parser import_contacts --contacts=[/path/to/contacts/csv]

Reading and exporting

You can read from the database using the sqlite3 client

sqlite3 messages.db

Some example queries:

Find unique email addresses

SELECT distinct from_address from messages;

Some column info

SELECT
  from_display AS full_email_address,
  from_address AS email_address,
  strftime('%d/%m/%Y', datetime(date, "unixepoch")) as date,
  subject,
  ('https://groups.google.com/a/YOUR_ORGANIZATION/forum/#!topic/YOUR_GROUP_NAME/' || messages.id) AS message_url
  FROM messages;

Find only email addresses which are not in Intercom

SELECT distinct messages.from_address
FROM messages
LEFT JOIN contacts ON contacts.email = messages.from_address
WHERE contacts.email IS NULL;

Filter messages you probably don't care about

SELECT * from messages
WHERE
  lower(body) NOT LIKE "%unsubscribe%"
  AND lower(from_address) NOT LIKE "%no-reply";

Creating CSVs

You can write the output of any query to a CSV file with the following:

headers on
.mode csv
.output your_filename.csv