Leftist Study Calls For Banning 'Small Clusters' Of Right-Wing Thought Criminals to Avoid 'Inflaming The Entire Hive'

How do you get rid of hate speech on social platforms? Until now, companies have generally tried two approaches. One is to ban individual users who are caught posting abuse; the other is to ban the large pages and groups where people who practice hate speech organize and promote their noxious views.

But what if this approach is counterproductive? That's the argument in an intriguing new paper out today in Nature from Neil Johnson, a professor of physics at George Washington University, and researchers at GW and the University of Miami. The paper, "Hidden resilience and adaptive dynamics of the global online hate ecology," explores how hate groups organize on Facebook and Russian social network VKontakte -- and how they resurrect themselves after platforms ban them.

As Noemi Derzsy writes in her summary in Nature:

Johnson et al. show that online hate groups are organized in highly resilient clusters. The users in these clusters are not geographically localized, but are globally interconnected by 'highways' that facilitate the spread of online hate across different countries, continents and languages. When these clusters are attacked -- for example, when hate groups are removed by social-media platform administrators (Fig. 1) -- the clusters rapidly rewire and repair themselves, and strong bonds are made between clusters, formed by users shared between them, analogous to covalent chemical bonds. In some cases, two or more small clusters can even merge to form a large cluster, in a process the authors liken to the fusion of two atomic nuclei. Using their mathematical model, the authors demonstrated that banning hate content on a single platform aggravates online hate ecosystems and promotes the creation of clusters that are not detectable by platform policing (which the authors call 'dark pools'), where hate content can thrive unchecked.

In the paper, the authors offer a choice metaphor for platforms' current approach:

These two approaches are equivalent to attempts to try to understand how water boils by looking for a bad particle in a sea of billions (even though there is not one for phase transitions), or the macroscopic viewpoint that the entire system is to blame (akin to thermodynamics). Yet, the correct science behind extended physical phenomena lies at the mesoscale in the self-organized cluster dynamics of the developing correlations, with the same thought to be true for many social science settings

So what to do instead? The researchers advocate a four-step approach to reduce the influence of hate networks.

Identify smaller, more isolated clusters of hate speech and ban those users instead. Instead of wiping out entire small clusters, ban small samples from each cluster at random. This would theoretically weaken the cluster over time without inflaming the entire hive. Recruit users opposed to hate speech to engage with members of the larger hate clusters directly. (The authors explain: "In our data, some white supremacists call for a unified Europe under a Hitler-like regime, and others oppose a united Europe. Similar in-fighting exists between hate-clusters of the KKK movement. Adding a third population in a pre-engineered format then allows the hate-cluster extinction time to be manipulated globally.) Identify hate groups with competing views and pit them against one another, in an effort to sow doubt in the minds of participants.

I find these strategies fascinating, even if I wonder how pragmatic they are. (Particularly the latter two, are basically diametrically opposed to existing platform policy.) The first point, which the authors document with a forbidding amount of math, strikes me as the most persuasive. (Another researcher, Natalie Bucklin, recently posted a similar analysis of Twitter's hate clusters that also found large groups connected by a few big nodes, just as Johnson's team did.)

Johnson's team is currently developing software that it hopes will aid regulators and software platforms as they consider new interventions into hate speech.