Productive Friction

Mastodon vs Twitter meme
Mastodon vs Twitter meme (via

Right now, the great #TwitterMigration to Mastodon is in full flood. The initial trickle of migrants when Elon Musk first indicated he was going to acquire Twitter surged when he finally followed through, sacked a large proportion of staff and contract workers, turned off various microservices including SMS two-factor authentication (accidentally or otherwise), and announced that Twitter might go bankrupt. Growing numbers of archaeologists opened accounts on Mastodon, and even a specific archaeology-focussed instance (server) was created at by Joe Roe.

Something most Twitter migrants experienced on first encounter with Mastodon was that it worked in a manner that was just different enough from Twitter to be somewhat disconcerting. This was nothing to do with tweets being called ‘toots’ (recently changed to posts following the influx of new users), or retweets being called ‘boosts’, or the absence of a direct equivalent to quote tweets. It had a lot to do with the federated model with its host of different instances serving different communities which meant that the first decision for any new user was which server to sign up with, and many struggled with this after the centralised models of Twitter (and Facebook, Instagram etc.) though older hands welcomed it as a reminder of how the internet used to be. It also had a lot to do with the feeds (be they Home, Local, or Federated) no longer being determined by algorithms that automatically promoted tweets but simply presenting posts in reverse chronological order. And it had to do with anti-harassment features that meant you could only find people on Mastodon if you knew their username and server, and the inability to search text other than hashtags. These were deliberately built into Mastodon, together with other, perhaps more obviously, useful features like Content Warnings on text and Sensitive Content on images, and simple alt-text handling for images.

As one Mastodon user, Thea Hutchings, described it:

I keep seeing people say Mastodon is nothing like Twitter. And they’re right. Mastodon is an echo of the old internet, it’s decentralised, chaotic. What you get depends on your sysadmin. You can’t search, everything has to be shared to you by a human. Networks split apart and rejoin. What you see is your unique connection to it. Is this good? Maybe. But for me that’s the internet I grew up with. No algorithms, no targeted adverts, just human interaction, and it was glorious.

Similarly, Catherine Flick (quoted in Stokel-Walker 2022) characterises exchanges on Mastodon as a “natural state of conversation” rather than the performative ebbs and flows encouraged by Twitter, although the reliance on boosts for visibility and lack of widespread broadcasting compared with Twitter is seen by some as a key limitation. Indeed, Clive Thompson (2022) has recently written about how Mastodon was specifically designed to be anti-viral, in contrast to Twitter which was designed to generate attention with surging trends that quickly dissipate to be replaced by others, all as a means of driving engagement and presenting advertising to consumers. Instead, as Thompson (2022) says:

Coming from the world of Twitter, where velocity and flocking behavior are common (and enjoyed by many people) it can seem weird to encounter a culture that finds friction useful and productive; a feature, and not a bug.

Friction is perhaps most commonly seen as a drag, a negative, something that entails effort to overcome, and this has been the initial experience of many Twitter migrants to Mastodon in recent days. This is not unusual—experiencing friction is commonplace when familiar software undergoes a significant upgrade (think of the interface changes between Windows 7, Windows 8, and Windows 10, for instance, or the introduction of the ribbon in Microsoft Office). We also encounter friction when starting to use new software (think of the initial bafflement experienced on first acquaintance with Arc/QGIS or GitHub, for instance). Some years ago, Paul Edwards characterised computational friction as “the struggle involved in transforming data into information and knowledge” (2010, 84), incorporating limitations of processor speed and memory capacity along with the costs of programming and validating the outputs. But this is not a purely hardware or software issue: for instance, digital data are typically seen to be easier to collect, store, rearrange, duplicate, share, and analyse so can in a sense involve less friction than analog data. Edwards characterises data friction as

the costs in time, energy, and attention required simply to collect, check, store, move, receive, and access data. Whenever data travel—whether from one place on Earth to another, from one machine (or computer) to another, or from one medium (e.g. punch cards) to another (e.g. magnetic tape)—data friction impedes their movement. (Edwards 2010, 84).

So friction is encountered across the life- and use-cycle of digital as well as analog data. As I’ve suggested elsewhere, in archaeological terms data friction initially arises through human agency in terms of both the original deposition and eventual materialization of what might become data, as well as through the range of non-human agencies that determine what might survive as potential data (Huggett 2022a, 283). Subsequent archaeological practices such as standardisation of terminologies and recording systems can be seen as attempts to reduce friction, although friction also emerges in efforts to re-use data across different contexts which requires data cleaning, recoding, and further standardisation to reduce inconsistencies and misalignments. In this light, friction is a problem to be overcome, although as Christine Borgman has observed, the means by which we try to reduce friction and increase interoperability through the use of devices like taxonomies, thesauri, and metadata can introduce new frictions through the demands imposed by their creation and maintenance (Borgman 2015, 80).

However, friction can paradoxically be beneficial or advantageous rather than entirely negative. For example, long-term users of Mastodon argue that what Thompson describes as the “well-engineered friction” designed into Mastodon can help reduce negative behaviours and protect minority and disadvantaged groups, despite being perceived by some as limitations relative to the Twitter experience. Bill Caraher’s characterisation of a ‘slow archaeology’ (e.g. Caraher 2019) would see friction as a means of slowing down processes to allow time and free up energy to reflect on and engage with data both in the field and on the desktop (see Huggett 2022b). In part, this is to resist big data-style methodologies by promoting a closer, more intimate—even conversational—relationship with data, a more nuanced approach that does not assume that data are self-evident or straightforward or that problems within the data can be simply overcome through the application of more data. Instead, slowness is

an approach that is less deliberate and more intuitive; less predictable, because more imaginative; less rational and more poetic; less conclusive and more friction-full, because more inclusive (Strauss 2018:57).

Much the same might be observed about the Mastodon experience as well as slow data practice.


Borgman, Christine L. 2015. Big Data, Little Data, No Data: Scholarship in the Networked World. Cambridge, Massachusetts: The MIT Press.

Caraher, W. 2019. ‘Slow Archaeology, Punk Archaeology, and the “Archaeology of Care”’. European Journal of Archaeology 22 (3): 372–85.

Edwards, P. 2010. A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. Cambridge, Mass: MIT Press.

Huggett, J. 2022a. ‘Data Legacies, Epistemic Anxieties, and Digital Imaginaries in Archaeology’. Digital 2 (2): 267–95.

Huggett, J. 2022b. ‘Is Less More? Slow Data and Datafication in Archaeology’. In Critical Archaeology in the Digital Age: Proceedings of the 12th IEMA Visiting Scholar’s Conference, edited by K. Garstki, 97–110. Cotsen Digital Archaeology Series. Los Angeles: UCLA Cotsen Institute of Archaeology Press.

Strauss, C. 2018. ‘All in Good Time’, in The Future of the New: Artistic Innovation in Times of Social Acceleration, edited by T. Lijster, pp.55–68. Valiz, Amsterdam.

Stokel-Walker, C. 2022 ‘Should I join Mastodon? A scientists’ guide to Twitter’s rival’, Nature, 10th November.

Thompson, Clive 2022 ‘Twitter alternative: how Mastodon is designed to be “antiviral”’, UX Collective, 9th November.

Leave a Reply

Your email address will not be published. Required fields are marked *