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. In our review of behavioural science research on online misinformation, we document a pattern of low exposure to false and inflammatory content that is concentrated among a narrow fringe with strong motivations to seek out such information. In response, we recommend holding platforms accountable for facilitating exposure to false and extreme content in the tails of the distribution, where consumption is highest and the risk of real-world harm is greatest. We also call for increased platform transparency, including collaborations with outside researchers, to better evaluate the effects of online misinformation and the most effective responses to it. Taking these steps is especially important outside the USA and Western Europe, where research and data are scant and harms may be more severe.

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. Furthermore, we show that the embeddings capture meaningful "axes" that encompass knowledge domains, such as an axis from "soft" to "hard" sciences or from "social" to "biological" sciences, which allow us to quantitatively ground periodicals on a given dimension. By offering novel quantification in the science of science, our framework may, in turn, facilitate the study of how knowledge is created and organized.

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. However, both are a relatively small proportion of the overall conversation. In contrast, news sources are shared at a much higher rate. These findings lead us to analyze the network created by the URLs referenced on the webpages shared by Twitter users. When looking at the combined network formed by all three of the source types, we find that the high quality health information network, the low quality misinformation network, and the news information network are all well connected with a clear community structure. While high and low quality sites do have connections to each other, the connections to and from news sources are more common, highlighting the central brokerage role news sources play in this information ecosystem. Our findings suggest that while low quality URLs are not extensively shared in the COVID-19 Twitter conversation, a well connected community of low quality COVID-19 related information has emerged on the web, and both health and news sources are connecting to this community.

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. We present a conceptual framework for the cumulative effect of all factors, which we call student capital. We develop a method for estimating student capital in groups of students and find that student capital is distributed exponentially in each of 140 cohorts of community college students. Students' ability to be successful does not behave like standard tests of intelligence. Instead, it acts like a limited resource, distributed unequally. The results suggest that rather than removing barriers related to easily measured characteristics, interventions should be focused on building up the skills and resources needed to be successful in school.

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. Our preliminary findings suggest that a meaningful spatio-temporal relationship exists between information flow and new cases of COVID-19, and while discussions about myths and links to poor quality information exist, their presence is less dominant than other crisis specific themes. This research is a first step toward understanding social media conversation about COVID-19.