What's Mastodon and what's it got to do with cybernetics?

The promise and pitfalls of decentralising social media infrastructure.

Picture of Kathy Reid

Written by: Kathy Reid
10 Nov 2022


Tree formation in the Otway Ranges taken by Kathy Reid, circa 2011
Tree formation in the Otway Ranges taken by Kathy Reid, circa 2011

If you’re a regular Twitter user, then you will have seen the recent ructions on the platform as SpaceX and Tesla founder, Elon Musk, took control of the platform in a US$44 billion acquisition. The move has sparked calls for #TwitterMigration to a platform called Mastodon. But what is Mastodon, and what’s it got to do with cybernetics?

Twitter has long been studied by academics. The relationships between the estimated 330 million users on the platform, and their interactions, have provided data sources for scholars in fields such as digital humanities 1 2, sociology 3 and human computer interaction 4. Twitter data has been used to measure influence 5, to identify hate speech 6, as a tool for health research 7 8 and even to predict flu trends 8. Mastodon, by contrast, is almost unheard of outside of the open source and GLAM (galleries, libraries, archives and museums) subcultures that have to-date championed its use. Very few academic papers make use of it as a data source, with some exceptions 9 10.

From a user perspective, the two platforms have similar features–a key reason why Mastodon is seen as an attractive destination for those questioning their continued engagement with Twitter. For example, users can ‘follow’ each other, and interact with content that is posted by liking or re-sharing with followers. The names given to these actions on the two platforms differ however. With Twitter, posting content is ‘tweeting’, while in Mastodon the same action is ‘tooting’. Sharing content to followers is ‘re-tweeting’ on Twitter, while it’s known as ‘boosting’ on Mastodon. There are also salient differences; Twitter suggests people to follow, while Mastodon relies much more heavily on users exploring timelines of content for keywords known as hashtags on both platforms.

By drawing on cybernetic principles, we can further compare and contrast Twitter and Mastodon, and gain a deeper understanding of the #TwitterMigration phenomenon–and the promises and pitfalls of attempts to decentralise social media platforms.


Firstly, we can think about where control of a system or platform is located. Even though the interactions among people on Twitter can be mapped as networks of interconnections, the platform itself is centrally controlled. It sets its own policies around content moderation, and raises revenue both through advertising and selling a monthly subscription with additional features. Twitter controls the algorithms that decide what content is shown and to whom, and although the platform provides detailed transparency reporting 11, users themselves have little control over the platform’s direction and feature roadmap.

By contrast, Mastodon is decentralised by design. Anyone can create an ‘instance’ of Mastodon, using open source software. The administrator of the instance can make decisions around whether that instance ‘federates’ - or joins and shares data with - other instances. This allows communities–particularly those with vulnerable cohorts–to protect themselves from instances hostile to their beliefs or interests. This provides users with more theoretical control over the platform; they can choose which instance to join based on how it federates with other instances, and the algorithms used for displaying content are transparent via the open source code. But in practice, this decentralised design comes with drawbacks.


Another cybernetic principle we often think about is the sustainability of a system, or the resource usage it demands.

There are two key costs for a software platform–employees and server infrastructure. While Twitter has around 7500 employees 12, funded through revenues from advertising and subscriptions, Mastodon and the instances run by administrators have no formal organisational structure, and are reliant on crowdfunding through platforms such as Patreon to cover server hosting costs. Some Mastodon instances charge a subscription fee to cover costs, such as the cloudisland.nz instance, run by prominent human rights advocate and cloud architect, Aurynn Shaw. Considerable devops expertise is required to run the technical stack required by Mastodon, which includes node.js and ruby software, and in many cases, this is provided on a volunteer basis by instance administrators. The unsustainability of volunteer open source developers and their efforts to generate recurring revenue through crowdsourcing has been written about at length by other authors–most notably Nadia Asparouhova (then Nadia Eghbal) 13 in her book Working in public and it serves as a follow on if you’re interested in this topic. The bottom line however is thus: running an instance of Mastodon comes with non-negligible costs in both server infrastructure and volunteer time.


And that takes us to the cybernetic principle of scale.

A large part of Twitter’s market capitalisation of US$44 billion is derived from the concept of network effect. Network effect states that the value of a network is proportional to the number of ‘nodes’ or users that the network has. Twitter has around 330 million members, and while it’s difficult to understand how many users there on Mastodon as a whole, due to federation, I do have information on a popular Australian-based instance, aus.social, administered by devops engineer, Shlee. Over the end of October to the first days of November, following Elon Musk’s acquisition of Twitter, the aus.social instance underwent rapid growth, as shown in the image below.

Growth of aus.social Mastodon instance.
Image credit: Ashley 'Shlee' Hull, aus.social administrator, used with permission

In just a few days, the number of users on the platform grew by nearly 600%. Interactions on the platform tripled. The aus.social server infrastructure is now under ‘load’ a technical term meaning that the infrastructure is approaching the limits of scale it was designed for. Ashley ‘Shlee’ Hull, the administrator of the aus.social instance, is working with the aus.social community to identify sustainable options for maintenance - both the server and human kinds.

And what of Twitter and scale? Elon Musk is reported to be about to implement a subscription model for ‘verified’ users, ostensibly to reduce reliance on advertising revenues. How will this affect Twitter’s user base? Will some of the 330 million make the #TwitterMigration to Mastodon? Only time will tell! But in the meantime, we can use our cybernetics principles of control, sustainability and scale to help make sense of what’s happening, and understand both the promise and pitfalls of decentralised social media platforms.

If you’re exploring Mastodon, you might find this quick start guide useful, as well as this list of academics on Mastodon, curated by Hendrik Erks, a PhD scholar from Sweden. And if you’re trying to find your Twitter followers, you’ll find this importing tool helpful–it searches your Twitter connections and identifies if they have their Mastodon link in their profile.

Reference list#

  1. Grandjean, M., 2016. A social network analysis of Twitter: Mapping the digital humanities community. Cogent Arts & Humanities, 3(1), p.1171458. 

  2. Takhteyev, Y., Gruzd, A. and Wellman, B., 2012. Geography of Twitter networks. Social networks, 34(1), pp.73-81. 

  3. Weller, K., Bruns, A., Burgess, J., Mahrt, M. and Puschmann, C. eds., 2013. Twitter and society. New York: Peter Lang. 

  4. Huberman, B.A., Romero, D.M. and Wu, F., 2008. Social networks that matter: Twitter under the microscope. arXiv preprint arXiv:0812.1045. 

  5. Anger, I. and Kittl, C., 2011, September. Measuring influence on Twitter. In Proceedings of the 11th International conference on knowledge management and knowledge technologies (pp. 1-4). 

  6. Pitsilis, G.K., Ramampiaro, H. and Langseth, H., 2018. Effective hate-speech detection in Twitter data using recurrent neural networks. Applied Intelligence, 48(12), pp.4730-4742. 

  7. Sinnenberg, L., Buttenheim, A.M., Padrez, K., Mancheno, C., Ungar, L. and Merchant, R.M., 2017. Twitter as a tool for health research: a systematic review. American journal of public health, 107(1), pp.e1-e8. 

  8. Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.H. and Liu, B., 2011, April. Predicting flu trends using Twitter data. In 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS) (pp. 702-707). IEEE.  2

  9. Zignani, M., Gaito, S. and Rossi, G.P., 2018, June. Follow the “mastodon”: Structure and evolution of a decentralized online social network. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 12, No. 1, pp. 541-550). 

  10. Cerisara, C., Jafaritazehjani, S., Oluokun, A. and Le, H., 2018. Multi-task dialog act and sentiment recognition on mastodon. arXiv preprint arXiv:1807.05013 

  11. Twitter transparency reporting webpage 

  12. Conger, K., Mac, R., Frenkel, S., Isaac, M., 2022. Elon Musk Is Said to Have Ordered Job Cuts Across Twitter - The New York Times 

  13. Eghbal, N. (2020). Working in public: the making and maintenance of open source software. Stripe Press. 

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