A Twitter live-data collector. Keep an eye on your disk space ;-)
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A Twitter live-data collector. Keep an eye on your disk space ;-)

What is This?

This project is largely an experiment that may one day grow into something useful for more than just a handful of people.

The idea is simple: Albatross is a way for you to capture everything said about something and then analyse the raw data yourself or let this site visualise it for you.

Say you're at a conference that's managing the conversation with the hashtag: #awesomeconference. You're participating when you can, but it would be really nice to be able to collect everything everyone said during the conference and draw some conclusions from it.

Maybe you want to do some analytics based on some natural language processing, or want to chart the number of times a particular phrase was mentioned within that hashtag. Whatever you want to do with the data, just fill out the form here with the hashtag in question, hit submit and when the conference ends, you've got all the data to play with.

Similarly this can be used for international events, or national disaster coverage. Plot on a map the tweets posted about your subject and when, or analyse the content of the tweets to see what different regions are saying about a particular subject.

Can You Do That For Me?

Analytics and visualisation is hard, and not everyone has the time or inclination to figure out how to parse the JSON blobs Twitter makes available via their API. Thankfully, Albatross has a bunch of built-in visualisations that might be sufficient for many people, but if you need something more customised, you can open an issue and I can give it a shot. Of course, contributions are welcome!

Data Format

The raw data is available as a xzip-compressed "fjson" file. This is just a plain text file, with one JSON object per line. You can decompress this file in Linux & Mac with the xz utility, or use a common program like WinRar in Windows.


Everything is dockerised, so you need:

  • Docker

  • Docker Compose

  • A .env file defining the following values:


    Note that the values aren't wrapped in quotes. You can thank Docker for that.

    You can also optionally set some other values here:

    • You can configure the externally-available ports for the web service and RabbitMQ by setting WEB_PORT=<number> and RABBITMQ_PORT=<number> respectively.
    • You can get more output from Albatross by changing the log level. Set DJANGO_LOG_LEVEL=INFO or even DJANGO_LOG_LEVEL=DEBUG if you want.
    • If you're planning on doing some development, you can turn global debugging on by setting DEBUG=true
    • You can also change Django's SECRET_KEY by setting that value here.


1. Setup your own Twitter app.

Albatross needs a CONSUMER_KEY and CONSUMER_SECRET from Twitter in order to operate. To get these, you'll need to log into Twitter and visit their app site. Once there, click the Create New App button, and put whatever you want into the form fields that follow. Just make sure that for the last box, Callback URL, you put in

Once you've submitted the form, Twitter should present you with a page that has 4 tabs, one of which is labelled Keys and Access Tokens. Click that, and it will reveal two values:

  • Consumer Key (API Key)
  • Consumer Secret (API Secret)

These are your TWITTER_CONSUMER_KEY and TWITTER_CONSUMER_SECRET values respectively. Put them into your .env file (mentioned above).

2. Clone the repo

Now that you have the credentials to identify yourself to Twitter, you just need the Albatross code. You can do this with git, which is handy 'cause you can use git later to keep your code up to date.

$ git clone https://github.com/danielquinn/albatross.git

If you don't have or want to use git, you can just download a copy of Albatross from Github and unzip it into a directory somewhere.

3. Add the environment config file mentioned above.

Before you run anything, you need to have that .env file mentioned above. At the very least, it should contain your values for TWITTER_CONSUMER_KEY and TWITTER_CONSUMER_SECRET as well as PYTHONUNBUFFERED=1.

$ cd albatross
$ ${EDITOR} .env

4. Start the containers

When the above is complete, you just need to tell Docker to run all of the required bits and keep them running. To do that, you use docker-compose:

$ docker-compose up

Note that the startup process is such that some of the components, like the collector & webserver may fail as the database isn't ready yet. That's ok, they should be re-started automatically at which point they'll retry to connect to the database. Eventually, all of the components will level out and Albatross will be ready.

After that, just visit https://localhost:8000 and login with Twitter to start your first collection.


Here's what it's doing under the hood:


State of the Project

I wrote this back in 2015 as an attempt to start a company doing this sort of thing for people & businesses. However, not long after I had a working model, Twitter started acting more and more hostile to projects that would dare store "their" data, so I gave up on it.

In 2018 however, I repurposed Albatross into a self-hosted model to help a friend do some research for her PhD. If it works for her, it may work for others, so I decided to polish it up a bit and re-license it under the AGPL3.