Is Online Hate Contagious? Why the Virus Metaphor Isn’t Quite Right
In this post, Mihaela Popa-Wyatt discusses her article “Online Hate: Is Hate an Infectious Disease? Is Social Media a Promoter?”, published in the Journal of Applied Philosophy. She argues that hate does not go viral but takes hold through repeated reinforcement — exactly what platforms are built to deliver—and draws out what follows for how we think about platform responsibility.
We often say that hate “goes viral.” The metaphor treats hateful belief like measles: one exposure and you may carry it. This picture is wrong. Hate does not spread like a virus. It rather spreads like joining a protest: people commit only once enough others they trust have committed first. Network scientists call this complex contagion.
The distinction matters for policy. Social media platforms are engineered to deliver precisely the dense, repeated reinforcement that complex contagion requires. They are not neutral carriers of hate. They are promoters of it. If that is right, the central question is not which posts to delete. It is who designs the environment and what they are permitted to optimise for.
The simple contagion model fails
The standard story runs like this. A hostile post infects a viewer. The viewer passes it on. Once enough people carry it, hate breaks out across the network.
But almost nobody becomes a committed racist from a single tweet. Hateful attitudes accumulate. They are absorbed from many sources over time and they take root in people who already feel uncertain, aggrieved, or hungry for belonging.
So the one-shot model cannot explain what we observe. Some people consume vast amounts of hostile content and remain unmoved. Others are transformed by far less. The difference is not how much they see. It is the structure of what they see: hostile content arriving repeatedly, from multiple sources, framed as what people like them believe. People are radicalised in groups by voices they trust.
The virus metaphor has one further shortcoming, and it’s the one that matters most. A virus passes through a passive host: the host does nothing. But people do something. They weigh what they hear, decide whom to trust, and choose what to pass on. This determines what intervention(s) are likely to succeed. If hate really were a pathogen, quarantine would be the answer: isolate carriers, remove contaminated material, wait. Because hate is instead something people adopt, under conditions that make adoption feel reasonable, the answer lies in those conditions: who builds them and what they are built to maximise.
Thresholds are the key
Complex contagion offers a better model. Its defining feature is a threshold: a person adopts only after receiving reinforcing signals from several different sources. Anything costly, risky, or norm-violating spreads this way, because risk is what makes social proof necessary. Adopting an unproven technology is one example; joining a protest is another. One friend marching is not enough; five friends marching changes the calculus. What does the work is not the number of exposures, but the number of sources.
Hateful attitudes behave the same way. People adopt them when their social environment signals that the attitudes are normal, shared, and safe to express. A single hostile post converts no one. A feed where hostility appears repeatedly, from many accounts, framed as common sense, slowly shifts a person’s sense of what “people like me” think.
Once a threshold is crossed, the recipient becomes a source. The person who was converted starts producing hateful content, supplying one of the reinforcing signals that will move somebody else across their own threshold.
This explains three things the virus model cannot. Hate clusters in tight communities rather than diffusing evenly across a network. Deradicalisation is hard, because removing a single contact leaves the surrounding web of reinforcing ties intact. And people who leave hateful communities often relapse, because the threshold structure that recruited them is still standing.
Social media is a promoter, not a medium
If hate spreads by complex contagion, then the environments that spread it best will be those that maximise reinforcement density. Consider what followed the Southport stabbings on 29 July 2024. Because the suspect was a minor, reporting restrictions made official identification legally impossible for three days, leaving an information vacuum. A fabricated name for the attacker, originating in a single unsourced post on X, was taken up by accounts presenting themselves as news outlets; a small site wrote it into an article, which an account with 2.8 million followers then cited as reporting. Within a day the name had accumulated over 30,000 mentions across more than 18,000 accounts.
What matters here is not the volume but the distribution. A user met the claim from many apparently distinct testifiers, some posing as journalistic authority. This is precisely the structure that a threshold model predicts will produce adoption. But the independence was illusory. All of it traced back to one unverified source, so the convergence carried no confirmatory weight. Platforms then supplied the same illusion at scale. The name trended on X and appeared as a suggested search on TikTok, and kept doing so for nine hours after police confirmed it was false. Recommender systems are indifferent to truth- value. What they optimise for is the very thing complex contagion feeds on: the appearance of many sources saying the same thing.
The mechanism generalises. Recommendation algorithms cluster similar users together, inflating the apparent prevalence of in-group views. Engagement-based ranking surfaces emotionally charged content, much of it hostile. High-follower accounts lend authority to hateful framings. And anonymity lowers the cost of expressing extreme views, so their visible frequency rises. This means that the threshold falls for everyone watching.
None of these features was designed to spread hate. Each nonetheless supplies precisely what complex contagion requires: hostile content encountered repeatedly, from sources that appear numerous, independent, and either similar to oneself or authoritative.
Social media is therefore not like air carrying a virus. It is an active promoter whose structural features systematically lower the threshold at which hate takes hold.
The harm is a design harm.
What follows for policy
Standard policy aims at the wrong target. Removing individual posts treats symptoms, while the ecology that produced them remains intact. Banning the loudest accounts helps only modestly because complex contagion does not depend on any single superspreader. What matters is the density of reinforcement in a user’s feed, and that survives the removal of any one node.
The right interventions are structural. Each targets a condition the mechanism requires.
· Break up homogeneous clusters. Complex contagion needs dense, clustered networks. Simple contagion travels well along long weak ties; complex contagion dies without local reinforcement. Clustering is the variable with the most theoretical leverage, and recommender systems currently maximise it.
· Reduce amplification of borderline-hostile content. Most reinforcement is supplied by material that breaks no rule. It sits below every removal threshold and is never moderated. What crosses a person’s threshold is volume, not severity.
· Change what ranking optimises for. Demoting engagement-bait treats the output of the metric. Engagement ranking selects for affective content because affect holds attention, so hostile material wins on the platform’s own terms. The metric is the thing to fix.
· Add friction to sharing inflammatory material. Reinforcement accumulates faster than verification. At Southport the police correction arrived after 30,000 mentions. Friction removes nothing. It changes the order in which claim and correction reach a user, and order is what determines whether correction works at all.
· Give counter-speech a reinforcement structure. Counter-speech faces the same threshold problem as hateful speech. One dissenting voice changes nothing; several arriving together might. No platform is built to deliver that. This is the only intervention that adds speech rather than restricting it.
None of these is sufficient alone, and each raises hard questions about platform power and free expression. But they aim at the mechanism rather than its output.
Who builds the room
Let’s return to the virus metaphor. If hate were a virus, quarantine would be the answer: isolate carriers, remove contaminated material, wait. That is roughly what content moderation attempts, and it is why content moderation underperforms. Hate is not caught; it is adopted by people who are reasoning, under conditions that make adoption look reasonable to them. Those conditions do not arise on their own. Someone builds them, maintains them, profits from them, and could build them otherwise.
This is why the familiar question of what speech is protected, and prohibited, cannot be the first one. It takes the environment as given and asks only what may circulate inside it. But the environment is not given. It is a product, with designers and a business model, and its design decides whether an unverified claim dies on arrival or reaches eighteen thousand thresholds by the following afternoon.
The line between protected and prohibited speech will still need drawing. But justice in public discourse is also a matter of who builds the conditions under which we speak, and what they are permitted to optimise for.
Mihaela Popa-Wyatt is Senior Lecturer in Philosophy at the University of Manchester. Her research spans philosophy of language, social philosophy, and public policy, and is organised around two related questions: how speech causes social harms, and how such harms can be reduced without undermining freedom of expression.
