GWU-based group developing the new science of online behavior
Published work
AJPH
We categorized 204 Facebook pages expressing vaccine opposition, extracting public posts through November 20, 2019. We analyzed posts from October 2009 through October 2019 to examine if pages’ content was coalescing.
David A. Broniatowski, Amelia M. Jamison, Neil F. Johnson, Nicolás Velasquez, Rhys Leahy, Nicholas Johnson Restrepo, Mark Dredze, Sandra C. Quinn
Heliyon
The distribution of whole war sizes and the distribution of event sizes within individual wars, can both be well approximated by power laws where size is measured by the number of fatalities. However the power-law exponent value for whole wars has a substantially smaller magnitude – and hence a flatter distribution – than for individual wars. We provide detailed numerical evidence that confirms that these numerically different power-law exponent values are interrelated in a simple way by the effect of aggregating fatalities from individual events within wars to whole wars. We offer intuition for this finding and hence strengthen the case for a unified description and understanding of human conflict across scales.
Michael Spagat, Stijnvan Weezel, D. Dylan Johnson Restrepo, Minzhang Zheng, Neil F. Johnson
Nature
Distrust in scientific expertise is dangerous. Opposition to vaccination with a future vaccine against SARS-CoV-2, the causal agent of COVID-19, for example, could amplify outbreaks, as happened for measles in 2019. Homemade remedies and falsehoods are being shared widely on the Internet, as well as dismissals of expert advice. There is a lack of understanding about how this distrust evolves at the system level. Here we provide a map of the contention surrounding vaccines that has emerged from the global pool of around three billion Facebook users. Its core reveals a multi-sided landscape of unprecedented intricacy that involves nearly 100 million individuals partitioned into highly dynamic, interconnected clusters across cities, countries, continents and languages. Although smaller in overall size, anti-vaccination clusters manage to become highly entangled with undecided clusters in the main online network, whereas pro-vaccination clusters are more peripheral. Our theoretical framework reproduces the recent explosive growth in anti-vaccination views, and predicts that these views will dominate in a decade. Insights provided by this framework can inform new policies and approaches to interrupt this shift to negative views. Our results challenge the conventional thinking about undecided individuals in issues of contention surrounding health, shed light on other issues of contention such as climate change, and highlight the key role of network cluster dynamics in multi-species ecologies.
Neil F. Johnson, Nicolas Velásquez, Nicholas Johnson Restrepo, Rhys Leahy, Nicholas Gabriel, Sara El Oud, Minzhang Zheng, Pedro Manrique, Stefan Wuchty, Yonatan Lupu
IEEE
A huge amount of potentially dangerous COVID-19 misinformation is appearing online. Here we use machine learning to quantify COVID-19 content among online opponents of establishment health guidance, in particular vaccinations (“anti-vax”). We find that the anti-vax community is developing a less focused debate around COVID-19 than its counterpart, the pro-vaccination (“pro-vax”) community. However, the anti-vax community exhibits a broader range of “flavors” of COVID-19 topics, and hence can appeal to a broader cross-section of individuals seeking COVID-19 guidance online, e.g. individuals wary of a mandatory fast-tracked COVID-19 vaccine or those seeking alternative remedies. Hence the anti-vax community looks better positioned to attract fresh support going forward than the pro-vax community. This is concerning since a widespread lack of adoption of a COVID-19 vaccine will mean the world falls short of providing herd immunity, leaving countries open to future COVID-19 resurgences. We provide a mechanistic model that interprets these results and could help in assessing the likely efficacy of intervention strategies. Our approach is scalable and hence tackles the urgent problem facing social media platforms of having to analyze huge volumes of online health misinformation and disinformation.
Richard F. Sear, Nicolás Velásquez, Rhys Leahy, Nicholas Johnson Restrepo, Sara El Oud, Nicholas Gabriel, Yonatan Lupu, Neil Johnson
Nature
Online hate and extremist narratives have been linked to abhorrent real-world events, including a current surge in hate crimes and an alarming increase in youth suicides that result from social media vitriol; inciting mass shootings such as the 2019 attack in Christchurch, stabbings and bombings; recruitment of extremists, including entrapment and sex-trafficking of girls as fighter brides; 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 family; 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 hate 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 behaviours such as financial fraud.
N. F. Johnson, R. Leahy, N. Johnson Restrepo, N. Velasquez, M. Zheng, P. Manrique, P. Devkota, S. Wuchty