Online hate and extremist narratives have been linked to abhorrent real-world events, including a current surge in hate crimes1,2,3,4,5,6 and an alarming increase in youth suicides that result from social media vitriol7; inciting mass shootings such as the 2019 attack in Christchurch, stabbings and bombings8,9,10,11; recruitment of extremists12,13,14,15,16, including entrapment and sex-trafficking of girls as fighter brides17; threats against public figures, including the 2019 verbal attack against an anti-Brexit politician, and hybrid (racist–anti-women–anti-immigrant) hate threats against a US member of the British royal family18; and renewed anti-western hate in the 2019 post-ISIS landscape associated with support for Osama Bin Laden’s son and Al Qaeda. Social media platforms seem to be losing the battle against online hate19,20 and urgently need new insights. Here we show that the key to understanding the resilience of online hate lies in its global network-of-network dynamics. Interconnected hate clusters form global ‘hate highways’ that—assisted by collective online adaptations—cross social media platforms, sometimes using ‘back doors’ even after being banned, as well as jumping between countries, continents and languages. Our mathematical model predicts that policing within a single platform (such as Facebook) can make matters worse, and will eventually generate global ‘dark pools’ in which online hate will flourish. We observe the current hate network rapidly rewiring and self-repairing at the micro level when attacked, in a way that mimics the formation of covalent bonds in chemistry. This understanding enables us to propose a policy matrix that can help to defeat online hate, classified by the preferred (or legally allowed) granularity of the intervention and top-down versus bottom-up nature. We provide quantitative assessments for the effects of each intervention. This policy matrix also offers a tool for tackling a broader class of illicit online behaviours21,22 such as financial fraud.
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The dataset is provided as Source Data. The open-source software packages Gephi and R were used to produce the networks in the figures. No custom software was used.
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N.F.J. is supported by US Air Force (AFOSR) grant FA9550-16-1-0247.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Peer review information Nature thanks Paul Gill, Nour Kteily and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Extended data figures and tables
a, b, Power laws for the KKK ecology (a) and the ecology of illicit financial activities (b). Their power-law exponents (α) are similar in a and b, and also consistent with c. c, The results from aggregating data from different thematic subsystems, each of which has a power-law distribution with an exponent (βi) distributed around 2.5. d, Summary of the simulation procedure. N power-law distributions are created with a power-law exponent distributed around 2.5. Power-law exponents were then sampled, followed by a power-law test. e, Distribution of the resulting power-law exponents from this simulation procedure, for different values of the mean number of points in the underlying distributions (mu values). The resulting power law exponents α are centred near 1.7, as observed in a and b.
a, The effects of policy 1, with on average more than 550 widely spaced time steps for τ = 10 and N = 104. If the size of an aggregate remains within the range smin to smax for a particular time period τ, that aggregate then fragments. b, The effects of policy 2. Colours represent different intervention starting times (tI) in units of days (vertical grey lines): green tI = 80, red tI = 120, blue tI = 200. Line types represent different percentages of individuals randomly removed (that is, banned) at time tI: dashed line 10%, dotted line 30%, solid line 50%. c, Results for policy 3 of the time to extinction (T) as a function of the initial population partition (N + P = 1,000 fixed, with N being the initial size of the hate population and P being the initial size of the anti-hate population) and the banning rate of the platform, from numerical simulations and also analytic theory. d, Policy 4 shows effect of different allocations of 100 peacekeepers in the hate-cluster versus anti-hate-cluster scenario. nc is the number of clusters of peacekeepers (that is, individuals of type C) that have size sc.
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Johnson, N.F., Leahy, R., Restrepo, N.J. et al. Hidden resilience and adaptive dynamics of the global online hate ecology. Nature 573, 261–265 (2019). https://doi.org/10.1038/s41586-019-1494-7
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