Social status and novelty drove the spread of online information during the early stages of COVID-19.

Access to online information has been crucial throughout the COVID-19 pandemic. We analyzed more than eight million randomly selected Twitter posts from the first wave of the pandemic to study the role of the author's social status (Health Expert or Influencer) and the informational novelty of the tweet in the diffusion of several key types of information. Our results show that health-related information and political discourse propagated faster than personal narratives, economy-related or travel-related news. Content novelty further accelerated the spread of these discussion themes. People trusted health experts on health-related knowledge, especially when it was novel, while influencers were more effective at propagating political discourse. Finally, we observed a U-shaped relationship between the informational novelty and the number of retweets. Tweets with average novelty spread the least. Tweets with high novelty propagated the most, primarily when they discussed political, health, or personal information, perhaps owing to the immediacy to mobilize this information. On the other hand, economic and travel-related information spread most when it was less novel, and people resisted sharing such information before it was duly verified.

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. Our analysis shows that the contact patterns of people in a given region are significantly influenced by the policies and behaviors of people in other, sometimes distant, regions. When just one-third of a state's social and geographic peer states adopt shelter-in-place policies, it creates a reduction in mobility equal to the state's own policy decisions. These spillovers are mediated by peer travel and distancing behaviors in those states. A simple analytical model calibrated with our empirical estimates demonstrated that the "loss from anarchy" in uncoordinated state policies is increasing in the number of noncooperating states and the size of social and geographic spillovers. These results suggest a substantial cost of uncoordinated government responses to COVID-19 when people, ideas, and media move across borders.

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. We find that ignoring assortativity and the joint distribution of influence and susceptibility leads traditional models to underestimate influence propagation by 21.7% on average, for a fixed seed set size. The traditional models and the empirical types that we specify here also identify substantially different seed sets, with only 19.8% overlap between them. The optimal seeds chosen under empirical influence models are relatively less well-connected and less central nodes, and they have more cohesive, embedded ties with their contacts. Hence, empirically motivated influence models have the potential to identify more realistic sets of key influencers in a social network and inform intervention designs that disseminate information or change attitudes and behaviours.

Subject-specific functional parcellation via prior based eigenanatomy.

We present a new framework for prior-constrained sparse decomposition of matrices derived from the neuroimaging data and apply this method to functional network analysis of a clinically relevant population. Matrix decomposition methods are powerful dimensionality reduction tools that have found widespread use in neuroimaging. However, the unconstrained nature of these totally data-driven techniques makes it difficult to interpret the results in a domain where network-specific hypotheses may exist. We propose a novel approach, Prior Based Eigenanatomy (p-Eigen), which seeks to identify a data-driven matrix decomposition but at the same time constrains the individual components by spatial anatomical priors (probabilistic ROIs). We formulate our novel solution in terms of prior-constrained ℓ1 penalized (sparse) principal component analysis. p-Eigen starts with a common functional parcellation for all the subjects and refines it with subject-specific information. This enables modeling of the inter-subject variability in the functional parcel boundaries and allows us to construct subject-specific networks with reduced sensitivity to ROI placement. We show that while still maintaining correspondence across subjects, p-Eigen extracts biologically-relevant and patient-specific functional parcels that facilitate hypothesis-driven network analysis. We construct default mode network (DMN) connectivity graphs using p-Eigen refined ROIs and use them in a classification paradigm. Our results show that the functional connectivity graphs derived from p-Eigen significantly aid classification of mild cognitive impairment (MCI) as well as the prediction of scores in a Delayed Recall memory task when compared to graph metrics derived from 1) standard registration-based seed ROI definitions, 2) totally data-driven ROIs, 3) a model based on standard demographics plus hippocampal volume as covariates, and 4) Ward Clustering based data-driven ROIs. In summary, p-Eigen incarnates a new class of prior-constrained dimensionality reduction tools that may improve our understanding of the relationship between MCI and functional connectivity.