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Mailing List Scraper

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mailingListScraper is a tool to extract data from public email lists in a format suitable for data analysis.


If you want to make some data analysis on a mailing list, first you need a dataset. mailingListScraper is a python tool enabling you to process the unstructured data available in public mailing list archives. The data is saved as .csv and .xml for easy statistical analysis, data modeling, text mining, machine learning, etc.

mailingListScraper is organized around Mailing List Archives. They usually store many mailing lists and provide a web interface to browse and read the emails.

Supported archives include:

Email Archive Lists # Emails # Default Mailing List
Hypermail 3 (list) 2,5m+ Linux Kernel Mailing List
MARC 3500+ (list) 80m+ git

User guide


Make sure to install scrapy and the dependencies first.

pip install -r requirements.txt

Clone the repo and cd into it. You're done!

Quick start

I strongly recommend that you identify yourself in the user-agent (mailingListScraper/ so that people can contact you if needed. Also, be mindful of the potential impact of your scraper on the server's load.

mailingListScraper is composed of several spiders. Each spider targets a specific email archive, which can host one or several mailing lists.

You can launch a spider running this command at the root level of the repo:

scrapy crawl {archiveName}

For instance, if I want to collect data from the Hypermail archive:

scrapy crawl hypermail

If the archive hosts multiple mailing lists, the spider only crawls one of them by default and lets you know which one. In the Hypermail case, that's the Linux Kernel Mailing List :

[hypermail] INFO: Crawling the LKML by default.

That's it! The spider is collecting data.

Collected data

The spider stores extracted emails in a data folder, containing:

  • {ArchiveName}ByEmail.csv: all metadata collected are stored in this file, with each row corresponding to an email. If you only crawl one mailing list, then the name is {mailingList}ByEmail.csv (and column mailingList is dropped).
  • {ArchiveName}{year}Bodies.xml: a XML file with the email body, in which each item is an email . If you only crawl one mailing list, then the name is {mailingList}{year}Bodies.xml (and node mailingList is dropped).

CSV file

Each row corresponds to an email, each column to one of the following fields:

Field Example Comment
mailingList lkml Migth be dropped if you only crawl through one mailing list.
emailId 20161017142556 The timestamp for received time ("received on 2016-10-17 at 14:25:56"). If two or more emails were received at the same time, we append a 0 (or more) at the end of the timestamp.
senderName Linus Torvalds If no name is found, will be the email.
senderEmail Might not be complete.
timestampSent 20161017142556+0500 Based upon previous field, a timestamp with timezone (if available). Will be "NA" if timeSent is "NA" or cannot be parsed. Some mailing list don't have one, so the whole column will be dropped.
timestampReceived 20161017142556+0500 Based upon previous field, a timestamp with timezone (if available). Will be "NA" if timeReceived is "NA" or cannot be parsed.
subject Re: [PATCH v1] oops Pretty obvious :)
url The url of the message.
replyto The url of the message the current email replies to.

When the scraper fails to extract the relevant information from the email, the field is marked as "NA".

XML file

The body of the emails is stored in an XML file, with some metadata, to make text mining easier. It's organized like this:

<?xml version="1.0" encoding="utf-8"?>
    <senderName>Linus Torvalds</senderName>
    <timestampReceived>2006-01-30 06:12:12-0400</timestampReceived>
    <subject>Re: [PATCH v1] oops</subject>
    <body>bla bla bla</body>

Each email is an item node.

This is how you would load the data with the tm package in R.


# Define custom XML reader
ml_reader <- readXML(
  spec = list(id = list("node", "/email/emailId"),
              content = list("node", "/email/body"),
              datetimestamp = list("node", "/email/timestampReceived"),
              subject = list("node", "/email/subject"),
              author = list("node", "/email/senderName"),
              author_email = list("node", "/email/senderEmail")
  doc = PlainTextDocument()

# Create custom source
ml_source <- function(x) {
  XMLSource(x, function(tree) XML::xmlChildren(XML::xmlRoot(tree)), ml_reader)

# Load documents as a VCorpus for example
ml <- VCorpus(ml_source("./lkml2017Bodies.xml"))


The spiders accept arguments from the command line. You can combine them to adjust the scope of your crawl.

Say I only care about the metadata of the emails sent in 1995, but I want to crawl all the lists in the Hypermail archive:

scrapy crawl hypermail -a mlist=all -a body=false -a year=1995


You can provide a comma separated list of mailing lists for a specific spider:

scrapy crawl archiveName -a mlist=mailinglist1,mailinglist2

To print the available mailing lists in an archive:

scrapy crawl hypermail -a mlist=print

To crawl every mailing list in the archive:

scrapy crawl hypermail -a mlist=all


Since downloading the body of each email can take up a lot of disk space, you can disable it:

scrapy crawl archiveName -a body=false

year, month

By default, the spiders crawl through every message in the mailing list. If you're only interested in a specific period of time, you can use the year and/or month argument for that.

You can focus on one year/month:

scrapy crawl marc -a year=2006
scrapy crawl marc -a month=01

Or you can give it a comma separated list of years/months:

scrapy crawl marc -a year=2006,2011
scrapy crawl marc -a month=01,06

Or even a range of months:

scrapy crawl marc -a year=2006:2008
scrapy crawl marc -a month=01:06

You can also combine the year and month arguments:

scrapy crawl marc -a year=2006:2008 -a month=01:06

Development and testing

I am currently developing this scraper to collect data for my PhD (EHESS in Paris, France). If you do see problems with the code, I'll be glad to review your Pull Requests ;-)


This scraper is developed in Python 3.5.2 with the scrapy framework.

Before you start working on your own spiders, you should set the LOG_LEVEL setting (mailingListScraper/ to DEBUG (or just uncomment the line).


Running tests and git pre-commit hook

You can run all tests with a simple ./ (see below for a detailed explanation of how the tests work).

If you want to only commit code that passes tests, you should install the git pre-commit hook by running:

ln -s ../../ .git/hooks/pre-commit

How the tests work

You can run the scrapy check command to run simple tests, with the built-in Scrapy contracts.

While contracts are fine for small verifications, they are not enough: you need to make sure that the data collected is consistent with your expectations. That's why some basic unit testing is provided in mailingListScraper/tests. Each spider and pipeline is to be tested with "real world" test cases.

The data for these test cases is provided in the pages directory. Cases are organized into subfolders, named after their spiders. In these subfolders, you'll find the test cases which consist of three files:

  • emailId.html: this is the page used as a Scrapy response to test the methods of the spider.
  • emailId.json: this is the data you expect to get from the spider. You can test the items extracted after they've been processed by the ItemLoader (itemOutput) and after they've been processed by the pipelines (pipelineOutput).
  • emailId.txt: that is the body you expect to retrieve from the page.

Each time you test a spider, it will iterate through a certain number of test cases. You can test a specific spider running this command at the root level of the repo:

python -m unittest mailingListScraper.tests.hypermail

Pipelines can also be tested:

python -m unittest mailingListScraper.tests.pipelines


The data is already publicly available online ; I am merely organizing it in a form that is convenient for data analysis. For instance, I cannot collect email addresses if the email archive hides it.

But when data is available and adds valuable information to the dataset, I will collect it. If your email address is not hidden, I do extract it to improve my chances of following an individual user over the years. Specifically, the same user might change her name but not her email address or the other way around. Collecting both the name and the email increases the probability of attributing these emails to the same person.

Keep in mind that I will only use the data collected with this scraper for research. In general, I will never use this data for spamming or targetting users with ads.

If you think I might have collected some of your emails on these public lists, feel free to contact me (@gaalcaras) if you have any questions or requests regarding your personal data.


A python web scraper for public email lists.





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