Let’s take the following as a given:
Mainstream social media is harmful because it puts a single company in control of the human social experience and places them in the role of cultural censor
If you agree and want to get on with addressing the problem, skip ahead to the next section.
When we say “control of the human social experience” we refer to something like Elinor Ostrom’s Institutional Analysis and Development Framework (here’s a good paper on applying IAD to digital institutions if you want to dive in), which describes platforms in terms of the following rules:
Operational Rules: Describe the interface and what actions a user can take, such as tweeting and retweeting and liking
Collective Rules: Describe the higher-level context in which the operational rules are utilized, like how the Twitter content feed orders tweets based on what users you follow, how popular the tweet was in terms of retweets and likes, and how old the content is
Constitutional Rules: Describe by what process the operational, collective, and constitutional rules can be changed
In a corporate-owned social media network, the corporation has complete control over the operational and collective rules, and most of the control over the constitutional rules. There may be some external influence, such as summoning the CEOs in front of Congress for questioning, or threatening to amend FCC regulations governing Internet platforms, or DMCA takedown requests. Regardless, the users of the platform, those most affected by operational and collective rules, have almost no say over those rules.
The only influence a user has over a platform is the option to leave, which en masse might impact the company’s bottom line. However, if we assume users on social media generally want to be social, then they’ll want to migrate to another social platform when they leave, and all the major social media companies have similarly tight control over their platforms with remarkably similar acceptable content and moderation policies.
When we describe platforms as cultural censors, we mean that they decide what content they will permit on their platform and what is a banable offense. Across social sites like Tumblr and Facebook and infrastructure sites like Paypal and Patreon, we’ve repeatedly seen companies take a puritanical stance against LGBT+, sex-positive, and sex-work content. Violet Blue (fantastic!) has written lots about this. Simultaneously, platforms are extremely hesitant to censor white supremacist or neo-nazi content, because they do not want to be accused of political censorship or bias by Congress or the White House, and the aforementioned content is increasing adjacent to Republican political talking points.
So, corporate-run social media implies a structure the users have no say in, with content limits the users have no say in, which favor harmful and icky views while inhibiting freedom of expression. There’s no way out by hopping to another corporate-run social media platform, because the next platform has the same problems and policies. “I’ll just build my own platform” leads to the same pattern with a different set of oligarchs, as we’ve seen (and I’ve written about before) with a range of “alt-tech” platforms like Voat, Parler, Gab, and BitChute that were created to host right-wing extremist content banned from their mainstream counterparts.
To address this problem we need a radical new kind of social media, with no central governors. This new media should not be susceptible to a small oligarchy enforcing social views, but ideally should have some conception of moderation, so community spaces can remove harassers, abusers, spammers, and hateful content.
The clear solution is a decentralized network, where content isn’t hosted on a single server hosted by a single entity. There can be collective storage of social media data, with collective agreement on what content to support and how.
Okay, great! So what does “decentralized” look like? A distributed hash table? A blockchain? A bunch of mirrored servers? Let’s look before we leap and consider common decentralized network models and their implications when used in social media of any sort.
I recently described distributed hash tables in more depth, but the one line summary is that in a DHT users are connected directly to multiple peers, and data in the form of key-value pairs (like a Python dictionary) are distributed across all peers with some redundancy, so anyone can quickly look up information associated with a known key. Participants use a centralized introduction server (called a “tracker” for torrents) to find their initial peers, but this introduction point has little information and can be easily replaced or made redundant.
DHTs have two significant limitations. The first is that all content must be framed as a key-value pair, and it’s not always obvious how to design systems around this constraint. Nevertheless, a variety of projects use DHTs at their core, including Hyperswarm, the underlying peer-discovery system used by Hypercore (previously known as “DAT protocol”), which in turn is the peer-to-peer content sharing system powering chat applications like Cabal. DHTs are also at the heart of GNUnet (another generic p2p content sharing system used for file-sharing and chat), and a similar key-value routing technology is used in Freenet, which is aimed at distributed file hosting, microblogging, version-control systems, and other append-only shared data. Finally, DHTs are used for routing in the Invisible Internet Project, and for connecting to onion services in Tor, so it’s safe to say that DHTs are a common design pattern in decentralized spaces.
The larger limitation of DHTs is that they produce a singleton. While a distributed hash table is “distributed” in the sense that content can be scattered across a wide range of peers, it is centralized in the sense that there is only a single DHT network shared by all users, with a single shared namespace, storing content for all peers in the network. This may not always be desirable: users may not want to host content for peers outside their social circle, may not want to reveal their IP address to peers outside their circle, or may simply want to extend the DHT and change its functionality in a way incompatible with the larger community. While it is technically possible to run multiple competing DHTs with no connection to one another, utilizing separate introduction servers, this is strongly disincentivized, since DHTs gain performance, redundancy, reliability, and storage capacity with scale.
Blockchains are a form of data storage with two interesting attributes:
They form an append-only log that cannot be rewritten (once a block has proliferated and several more blocks are added to “solidify” its presence in the chain)
They are highly redundant
If those two attributes are not both valuable for your application, then a blockchain is the wrong choice for you. There are alternative solutions to append-only logs (like signed messages stored in a merkle tree, or Paxos), and to data redundancy (like a DHT, or client-stored messages that can be re-uploaded to peers later, as in sneakernets). But let’s look at the structure of a blockchain network.
Blockchain network traffic looks remarkably similar to DHT traffic, except that instead of using key-value pairs, every peer stores the complete data log:
This certainly provides more redundancy than a DHT, and maybe that level of redundancy is important if you’re building a ledger of all financial transactions ever and your entire economy relies on its stability. For most use-cases, the redundancy afforded by a DHT is sufficient, and requires far less storage for each peer. Blockchains also imply a significant computational cost to write any new data if the blockchain uses proof-of-work to ensure immutability. It’s an expensive and over-applied data structure.
We can address some of the limitations of blockchain using a sidechain, where the primary blockchain (or another data structure like a merkle tree) includes references to the heads of miniature blockchains that are smaller and can be updated more quickly. These sidechains can follow simpler rules than a public blockchain, such as allowing a user with a private key to craft new blocks instead of using a “proof of work” algorithm.
Signal, the centralized but end-to-end encrypted chat software, uses a chat protocol with some similarities to a blockchain. In the Signal protocol, each message in a conversation includes a reference to both the previous message that user sent, and the most recent messages that user has seen. This means Signal’s central server can’t ommit messages without clients noticing, and at worst can deny all service to a user. However, there is no proof-of-work involved in this chain; the only requirement for adding a new block is that it must be signed by the user, eliminating one of the largest limitations of a full blockchain.
Sidechains are also at the heart of Keybase, a Slack-like encrypted messaging and file hosting system that maintains a chain for each user to immutably store information about the user’s identity. Keybase also maintains each shared folder as a blockchain of write operations. Notably, however, Keybase is a centralized system that uses blockchains and signature verification to keep the server honest. The blockchains serve as a tamper-evidence mechanism that makes it difficult for the server to revert or manipulate messages (along with aggressive client caching), but the Keybase server is a sole central data repository for the network.
As with DHTs, blockchain networks form singletons (even if not run on a central server like Keybase), and running parallel blockchains or forking a chain is frowned upon because it shrinks the network and dilutes the benefit of a single shared ground truth.
Federation is an interesting combination of centralization and decentralization. From the user’s perspective, federated social networks work much like mainstream networks, except that they have several Facebook- or Twitter-like servers to choose from. Each server operates as an independent miniature social community. Server operators, however, have a different experience. Each operator can choose to federate with another server, bidirectionally linking the two servers, exchanging messages to create a larger collaborative social network. Collections of federated servers are referred to as a fediverse.
The most well-known federated social network is Mastodon, a Twitter-like cluster of servers. Each server has its own content policies and moderators, and usually only federates with servers with similar content policies. This lessens the likelihood of the network implementing extremely puritanical social policies, and allows easy migration if moderators on a single server go power-mad. When Gab (an alt-right Twitter clone) abandoned their own software stack and became a Mastodon instance they were universally condemned, and most other server operators refused to federate with the Gab instance, isolating them on their own server and proving the effectiveness of Mastodon’s moderation strategy.
Unfortunately, Mastodon also suffers from the singleton problem. While servers can federate, it is difficult to convince an operator of a major server to create a bidirectional link with a minor one, and there is little incentive to run a Mastodon server and less incentive if it is unfederated with major servers. As a result, three Mastodon servers contain almost 60% of the known Mastodon population.
The Diaspora Network, a decentralized Facebook-like network, also follows a federation model. Of note, they popularized clustering contacts by “aspects” of your life, so you can easily share contact with some categories of peers and not others.
It’s worth pointing out that federation is far from a new concept. Internet Relay Chat also provides functionality for federating servers, synchronizing chatrooms and messages between two servers, though the process is very rarely used since it grants operators on both servers extreme power over conversations that occur on the other. Similarly, NNTP (the network news transfer protocol underlying Usenet) allows servers to exchange news postings and comments to create a shared community. In Usenet’s case federation was commonplace, and resulted in a singleton network similar to Reddit with far less moderation.
Pubs (in the “public house”, inn/bar sense of the word) invert the idea of federation. Instead of users connecting to a single server and allowing the servers to interlink, users now connect to several servers, and serve as the links. In a pub model, servers are reduced to the role of content caches and introduction points: They allow users to leave messages for other users to find, allowing two participants to communicate asynchronously without being online at the same time for a peer-to-peer connection. They also allow users to leave messages publicly on the server, making it possible to introduce themselves and meet other users.
Since users are connected to multiple servers at once, they can post the same message to multiple servers concurrently, and rely on clients to recognize and combine the duplicates. This means users are not bound to a single server where they host their content, as in a federated service, but can store their content feed on multiple servers, more akin to a distributed hash table. Since servers have no federation, there is little cost to running a pub server. Unlike in federated spaces, a small pub can be valuable by providing a closed but shared conversation space, representing a tighter group of friends, or colleagues with a similar and obscure interest. Users can choose to only post some of their messages to these private spaces, moving between more public and more private content feeds.
The most popular pub-model network in use today is Secure Scuttlebutt, which works both via pub servers and via syncing with peers over WiFi networks, exchanging both their own messages and cached messages from friends and friends-of-friends (gossip). Notably, Scuttlebutt is offline-first: the entire “content feed” is stored locally, so you can browse and reply to messages while offline, and then re-sync when you next connect to a pub or are on a LAN with another peer. The entire network can theoretically run without pubs, purely on local network exchanges, and place no authority on pubs at all. Without a reliance on central servers there is also no clear opportunity for community moderation. Scuttlebutt supports a peer blocking individual users and hiding them from their content feed, but this scales poorly on a larger network.
The Open Privacy Research Society is working on their own pub-based chat network, Cwtch, with the twist that pubs can’t read the messages they host. Cwtch is more like a signal group chat, sans the reliance on the central Signal servers, by caching messages in a variety of servers and storing them locally. Cwtch operates entirely over Tor, reaching pubs and other peers via Tor onion services. When two peers are online at the same time they can exchange their message logs from the group, filling in each-other’s blanks, and using a server only to leave messages asynchronously when peers are offline.
Pub-style networks have the distinct advantage of only caching content relevant to the user (or close to them in a social graph). Servers need to store considerably more content, but can auto-delete older messages to limit the load, since messages will live on in users’ local storage.
DHTs, Blockchains, Federation, and Pubs provide distinctly anti-capitalist models for sharing social content without a patron corporation facilitating discussion. Each decentralized model has unique characteristics shaping the kinds of information sharing that are possible, the opportunity (and dangers) for moderation, and the kind of clustering that is likely to result. I’m personally most excited about pubs, while acknowledging the utility of DHT networks, but all four paradigms have borne fruit already and should be pursued.