The online competition between pro- and anti-vaccination views.

Distrust in scientific expertise1-14 is dangerous. Opposition to vaccination with a future vaccine against SARS-CoV-2, the causal agent of COVID-19, for example, could amplify outbreaks2-4, as happened for measles in 20195,6. Homemade remedies7,8 and falsehoods are being shared widely on the Internet, as well as dismissals of expert advice9-11. There is a lack of understanding about how this distrust evolves at the system level13,14. 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 change11, and highlight the key role of network cluster dynamics in multi-species ecologies15.

Offline events and online hate.

Online hate speech is a critical and worsening problem, with extremists using social media platforms to radicalize recruits and coordinate offline violent events. While much progress has been made in analyzing online hate speech, no study to date has classified multiple types of hate speech across both mainstream and fringe platforms. We conduct a supervised machine learning analysis of 7 types of online hate speech on 6 interconnected online platforms. We find that offline trigger events, such as protests and elections, are often followed by increases in types of online hate speech that bear seemingly little connection to the underlying event. This occurs on both mainstream and fringe platforms, despite moderation efforts, raising new research questions about the relationship between offline events and online speech, as well as implications for online content moderation.

Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning.

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.

Inferring mechanisms for global constitutional progress.

Constitutions help define domestic political orders, but are known to be influenced by international mechanisms that are normative, temporal and network based. Here we introduce the concept of the 'provision space'-the set of all legal provisions existing across the world's constitutions, which grows over time. We make use of techniques from network science and information retrieval to quantify and compare temporal and network effects on constitutional change, which have been the focus of previous work. Furthermore, we propose that hierarchical effects-a set of mechanisms by which the adoption of certain constitutional provisions leads to or facilitates the adoption of additional provisions-are also crucial. These hierarchical mechanisms appear to play an important role in the emergence of new political rights, and may therefore provide a useful roadmap for advocates of those rights.