Understanding the rationales and information environments for early, late, and nonadopters of the COVID-19 vaccine.

Anti-vaccine sentiment during the COVID-19 pandemic grew at an alarming rate, leaving much to understand about the relationship between people's vaccination status and the information they were exposed to. This study investigated the relationship between vaccine behavior, decision rationales, and information exposure on social media over time. Using a cohort study that consisted of a nationally representative survey of American adults, three subpopulations (early adopters, late adopters, and nonadopters) were analyzed through a combination of statistical analysis, network analysis, and semi-supervised topic modeling.

Misunderstanding the harms of online misinformation.

The controversy over online misinformation and social media has opened a gap between public discourse and scientific research. Public intellectuals and journalists frequently make sweeping claims about the effects of exposure to false content online that are inconsistent with much of the current empirical evidence. Here we identify three common misperceptions: that average exposure to problematic content is high, that algorithms are largely responsible for this exposure and that social media is a primary cause of broader social problems such as polarization.

Neural embeddings of scholarly periodicals reveal complex disciplinary organizations.

Understanding the structure of knowledge domains is one of the foundational challenges in the science of science. Here, we propose a neural embedding technique that leverages the information contained in the citation network to obtain continuous vector representations of scientific periodicals. We demonstrate that our periodical embeddings encode nuanced relationships between periodicals and the complex disciplinary and interdisciplinary structure of science, allowing us to make cross-disciplinary analogies between periodicals.

Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams.

This article investigates the prevalence of high and low quality URLs shared on Twitter when users discuss COVID-19. We distinguish between high quality health sources, traditional news sources, and low quality misinformation sources. We find that misinformation, in terms of tweets containing URLs from low quality misinformation websites, is shared at a higher rate than tweets containing URLs on high quality health information websites.

The shape of educational inequality.

Hundreds of thousands of students drop out of school each year in the United States, despite billions of dollars of funding and myriad educational reforms. Existing research tends to look at the effect of easily measurable student characteristics. However, a vast number of harder-to-measure student traits, skills, and resources affect educational success.

A first look at COVID-19 information and misinformation sharing on Twitter.

Since December 2019, COVID-19 has been spreading rapidly across the world. Not surprisingly, conversation about COVID-19 is also increasing. This article is a first look at the amount of conversation taking place on social media, specifically Twitter, with respect to COVID-19, the themes of discussion, where the discussion is emerging from, myths shared about the virus, and how much of it is connected to other high and low quality information on the Internet through shared URL links.