Providing normative information increases intentions to accept a COVID-19 vaccine.

Despite the availability of multiple safe vaccines, vaccine hesitancy may present a challenge to successful control of the COVID-19 pandemic. As with many human behaviors, people's vaccine acceptance may be affected by their beliefs about whether others will accept a vaccine (i.e.

Identity effects in social media.

Identity cues appear ubiquitously alongside content in social media today. Some also suggest universal identification, with names and other cues, as a useful deterrent to harmful behaviours online. Unfortunately, we know little about the effects of identity cues on opinions and online behaviours.

A causal test of the strength of weak ties.

The authors analyzed data from multiple large-scale randomized experiments on LinkedIn's People You May Know algorithm, which recommends new connections to LinkedIn members, to test the extent to which weak ties increased job mobility in the world's largest professional social network. The experiments randomly varied the prevalence of weak ties in the networks of over 20 million people over a 5-year period, during which 2 billion new ties and 600,000 new jobs were created. The results provided experimental causal evidence supporting the strength of weak ties and suggested three revisions to the theory.

Global survey on COVID-19 beliefs, behaviours and norms.

Policy and communication responses to COVID-19 can benefit from better understanding of people's baseline and resulting beliefs, behaviours and norms. From July 2020 to March 2021, we fielded a global survey on these topics in 67 countries yielding over 2 million responses. This paper provides an overview of the motivation behind the survey design, details the sampling and weighting designed to make the results representative of populations of interest and presents some insights learned from the survey.

Interdependent program evaluation: Geographic and social spillovers in COVID-19 closures and reopenings in the United States.

In an interconnected world, understanding policy spillovers is essential. We propose a program evaluation framework to measure policy spillover effects and apply that framework to study the governmental responses to COVID-19 in the United States. Our analysis suggests the presence of social spillovers.

Interdependence and the cost of uncoordinated responses to COVID-19.

Social distancing is the core policy response to coronavirus disease 2019 (COVID-19). But, as federal, state and local governments begin opening businesses and relaxing shelter-in-place orders worldwide, we lack quantitative evidence on how policies in one region affect mobility and social distancing in other regions and the consequences of uncoordinated regional policies adopted in the presence of such spillovers. To investigate this concern, we combined daily, county-level data on shelter-in-place policies with movement data from over 27 million mobile devices, social network connections among over 220 million Facebook users, daily temperature and precipitation data from 62,000 weather stations, and county-level census data on population demographics to estimate the geographic and social network spillovers created by regional policies across the United States.

Publisher Correction: Social influence maximization under empirical influence models.

In the version of this Letter originally published, in the key for Fig. 1 the red square was mistakenly labelled 'Low influence' and 'High susceptibility' but should have been labelled 'High influence' and 'Low susceptibility'. This has now been corrected.

Social influence maximization under empirical influence models.

Social influence maximization models aim to identify the smallest number of influential individuals (seed nodes) that can maximize the diffusion of information or behaviours through a social network. However, while empirical experimental evidence has shown that network assortativity and the joint distribution of influence and susceptibility are important mechanisms shaping social influence, most current influence maximization models do not incorporate these features. Here, we specify a class of empirically motivated influence models and study their implications for influence maximization in six synthetic and six real social networks of varying sizes and structures.

The spread of true and false news online.

We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times.

Exercise contagion in a global social network.

We leveraged exogenous variation in weather patterns across geographies to identify social contagion in exercise behaviours across a global social network. We estimated these contagion effects by combining daily global weather data, which creates exogenous variation in running among friends, with data on the network ties and daily exercise patterns of ∼1.1M individuals who ran over 350M km in a global social network over 5 years.

Social influence bias: a randomized experiment.

Our society is increasingly relying on the digitized, aggregated opinions of others to make decisions. We therefore designed and analyzed a large-scale randomized experiment on a social news aggregation Web site to investigate whether knowledge of such aggregates distorts decision-making. Prior ratings created significant bias in individual rating behavior, and positive and negative social influences created asymmetric herding effects.

Identifying influential and susceptible members of social networks.

Identifying social influence in networks is critical to understanding how behaviors spread. We present a method that uses in vivo randomized experimentation to identify influence and susceptibility in networks while avoiding the biases inherent in traditional estimates of social contagion. Estimation in a representative sample of 1.

Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks.

Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.