Human social sensing is an untapped resource for computational social science.

The ability to 'sense' the social environment and thereby to understand the thoughts and actions of others allows humans to fit into their social worlds, communicate and cooperate, and learn from others' experiences. Here we argue that, through the lens of computational social science, this ability can be used to advance research into human sociality. When strategically selected to represent a specific population of interest, human social sensors can help to describe and predict societal trends. In addition, their reports of how they experience their social worlds can help to build models of social dynamics that are constrained by the empirical reality of human social systems.

You are only as safe as your riskiest contact: Effective COVID-19 vaccine distribution using local network information.

When vaccines are limited, prior research has suggested it is most protective to distribute vaccines to the most central individuals - those who are most likely to spread the disease. But surveying the population's social network is a costly and time-consuming endeavour, often not completed before vaccination must begin. This paper validates a local targeting method for distributing vaccines. That is, ask randomly chosen individuals to nominate for vaccination the person they are in contact with who has the most disease-spreading contacts. Even better, ask that person to nominate the next person for vaccination, and so on. To validate this approach, we simulate the spread of COVID-19 along empirical contact networks collected in two high schools, in the United States and France, pre-COVID. These weighted networks are built by recording whenever students are in close spatial proximity and facing one another. We show here that nomination of most popular contacts performs significantly better than random vaccination, and on par with strategies which assume a full survey of the population. These results are robust over a range of realistic disease-spread parameters, as well as a larger synthetic contact network of 3000 individuals.

When a man hits a woman: moral evaluations and reporting violence to the police.

We use experimental data from a nationally representative sample to examine whether gender and the victim's relationship to the offender affect attitudes about the seriousness of the offense and whether the offense should be reported to the police. We find that respondents are particularly likely to condemn men's assaults on women, and to favor reporting them. The pattern appears to reflect both greater moral condemnation of men's assaults on women and the belief that the victims of these assaults are in greater danger. In general, moral judgments and attitudes toward reporting do not depend on the gender, age, level of education, or political ideology of the respondent. Condemnation of men's violence against women, and support for police intervention when it occurs, are apparently widespread across different segments of the population.